Noise detection method, noise detection apparatus, simulation method, simulation apparatus, and communication system

ABSTRACT

For voltage values (observed noise sequence) in an electronic power line (communication medium) which are obtained at a predetermined interval, initial values of noise characteristics based on a statistic of the observed noise sequence itself are decided by a moment method (S 301  to S 307 ), the noise characteristics (state transition probabilities and state noise power) for maximization of the likelihood of the observed noise sequence are obtained from the initial values by MAP (Maximum A Posteriori) estimation using a Baum-Welch algorithm (S 309  to S 312 ), a state sequence is estimated from the obtained noise characteristics, and an impulsive noise at each time point is detected.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Nonprovisional application claims priority under 35 U.S.C. §119(a)on Patent Application No. 2009-150251 filed in Japan on Jun. 24, 2009,No. 2009-266707 filed in Japan on Nov. 24, 2009 and No. 2010-131191filed in Japan on Jun. 8, 2010, the entire contents of which are herebyincorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a communication system including aplurality of communication apparatuses. In particular, the presentinvention relates to a noise detection method, a noise detectionapparatus, a simulation method, a simulation apparatus and acommunication system which are capable of automatically detecting animpulsive noise, generated suddenly in a communication medium, from astatistical property of the observed noise itself.

2. Description of Related Art

Recently, in each field, there has been utilized a system in which aplurality of communication apparatuses are connected and functions areallocated to the respective communication apparatuses to mutuallyexchange data, thereby allowing the apparatuses to carry out variousprocesses in conjunction with each other. In a communication system, thequality of communication is influenced by impulsive noises generated ina communication medium through which the communication apparatuses areconnected to each other. Accordingly, it is necessary to take measuresto prevent impulsive noises, or to realize communication so as not to beinfluenced by impulsive noises.

In the field of in-vehicle LAN (Local Area Network) provided in avehicle, ECUs (Electronic Control Units) functioning as communicationapparatuses are used, and the ECUs are allowed to carry out specializedprocesses to mutually exchange data, thereby realizing various functionsas a system. Vehicle control is shifting from mechanical control towardelectrical control, and specialization of functions of respective ECUsand functions realized by a system are on the increase. In accordancewith this, the number and types of communication apparatuses areincreased, and the number of communication lines through which thecommunication apparatuses are connected to each other is also increased.Further, with an increase in the amount of data received and transmittedby a communication system, it is necessary to receive and transmit alarge amount of data at a higher speed.

In regard to this, attention is being given to PLC (Power LineCommunication) for realizing communication by superimposing acommunication carrier wave on an existing power line, and in addition,the application of PLC to in-vehicle LAN has been proposed (see PatentDocument 1, for example). The application of an in-vehicle PLC system toin-vehicle LAN can achieve a reduction in the number of lines, thusmaking it possible to expect various effects including: a reduction inthe weight of a vehicle; an improvement in fuel efficiency; andeffective utilization of a space in a vehicle.

In an in-vehicle PLC system, data has to be received and transmittedwith low delay and high reliability for safety reasons in particular.However, since an actuator is connected to a power line in in-vehiclePLC, a high amplitude impulsive noise is generated suddenly due tooperation and/or suspension of the actuator. Therefore, it is necessaryto: automatically detect the impulsive noise from a statistical propertyof the observed noise itself; conduct detailed preliminary studies on acommunication method or the like effective for the impulsive noise thatdiffers depending on a vehicle type or an option; and appropriatelyselect a communication method, a frequency and a communicationparameter.

As a method for detecting an impulsive noise, a method for detectingwhether or not there is an impulsive noise based on an amplitudethreshold and/or distribution of length of a given observation window(window size) has conventionally been used (see Non-Patent Documents 1,2, 3 and 4).

[Prior Art Reference] [Patent Document]

[Patent Document 1] Japanese Patent Application Laid-Open No.2006-067421

[Non-Patent Document]

[Non-Patent Document 1] M. Zimmermann and K. Dostert, “Analysis andmodeling of impulsive noise in broad-band power-line communications”,IEEE Trans. Electromagn. Compat., vol. 44, no. 1, pp. 249-258, February2002

[Non-Patent Document 2] M. G. S'anchez, L. de Haro, M. C. Ram'on, A.Mansilla, C. M. Ortega, and D. Oliver, “Impulsive noise measurements andcharacterization in a UHF digital TV channel”, IEEE Trans. Electromagn.Compat., vol. 41, no. 2, pp. 124-136, May 1999

[Non-Patent Document 3] V. Degardin, M. Lienard, P. Degauque, E. Simon,and P. Laly, “Impulsive noise characterization of in-vehicle power linechannels”, IEEE Trans. Electromagn. Compat., vol. 50, no. 4, pp.861-868, November 2008

[Non-Patent Document 4] I. Mann, S. Mclaughlin, R. K. W. Henkel, and T.Kessler, “Impulse generation with appropriate amplitude, length,inter-arrival, and spectral characteristics”, IEEE J. Sel. AreasCommun., vol. 20, no. 5, pp. 901-912, June 2002

SUMMARY OF THE INVENTION

An impulsive noise, which is generated from an actuator in in-vehiclePLC, has been detected insufficiently by the foregoing detection method.FIGS. 38 to 40 are waveform diagrams illustrating examples of impulsivenoises generated in a power line. In each of FIGS. 38 to 40, thehorizontal axis represents time while the vertical axis represents avoltage value, and FIGS. 39 and 40 are each obtained by partiallyenlarging FIG. 38.

As illustrated in FIGS. 39 and 40, the noises generated in the powerline of the in-vehicle PLC are damped sine waves. Accordingly, when anoise is detected based on whether or not an amplitude value is equal toor higher than a threshold (Non-Patent Documents 1 and 2), a singleimpulsive noise will be detected in such a manner that it is subdividedinto a plurality of noises (1 to 3) as illustrated in FIG. 40.

In regard to this, a method for detecting an impulsive noise based on adistribution value of length of an observation window is, for example,known (Non-Patent Documents 3 and 4), but a subjective detectioncondition set by an observer of a waveform is often included in decidingan observation window length used for measurement and a distributionvalue threshold. Thus, there is a disadvantage that a section of animpulsive noise to be detected is influenced by the foregoing impulsivenoise detection condition. Accordingly, even if noise detection fromwhich human subjectivity is removed is desired, such noise detection hasnever been realized.

The timing of operation of the actuator serving as a source ofgeneration of an impulsive noise is event-driven. In the case of a noisegenerated regularly, it is only necessary to estimate the reliability ofa signal in a regular period to be low. For example, in the case ofindoor PLC, it is known that a period of generation of an impulsivenoise is synchronized with that of a commercial power supply, and animpulsive noise can be detected with relatively high accuracy bydetecting the impulsive noise based on this period. However, anevent-driven operation, i.e., an actuator operation such aslocking/unlocking of an electric door lock of a vehicle, is carried outin response to an operation corresponding to turning ON/OFF of a switchby a driver or a fellow passenger, and an impulsive noise is generatedfrom a door lock actuator or the like in response to turning ON/OFF ofthe switch of the door lock, thus making it impossible to learn theforegoing temporal characteristics.

The present invention has been made in view of the above-describedcircumstances, and its object is to provide a noise detection method anda noise detection apparatus which are capable of removing subjectivedetection conditions to the extent possible, and automatically andaccurately detecting an impulsive noise, generated in a communicationmedium, from a statistical property of the observed noise itself.

Another object of the present invention is to provide a noise detectionmethod and a noise detection apparatus which are capable of taking apower line of in-vehicle PLC, for example, into consideration as acommunication medium, and accurately detecting an impulsive noisegenerated suddenly in response to an operation of an actuator connectedto the power line.

Still another object of the present invention is to provide: asimulation method and a simulation apparatus which reproduce animpulsive noise with high accuracy based on a feature of the impulsivenoise detected in a such manner that subjective detection conditions areremoved to the extent possible; and a communication system capable ofusing, based on the feature of the impulsive noise, a frequency thatminimizes the influence of the impulsive noise.

Yet another object of the present invention is to provide a noisedetection method and a noise detection apparatus which are capable ofmore accurately detecting, using statistical information, an impulsivenoise generated in a communication medium.

In the present invention, using an apparatus for detecting a noise in acommunication medium, signal levels (e.g., voltage values, currentvalues and/or power values) in the communication medium of acommunication system are measured at a predetermined interval (that issampling interval). The noise detection apparatus extracts an observednoise sequence (i.e., a time sequence of signal levels n from a timepoint 1 to a time point K), which is a time sequence of signal levels atrespective time points k for a measurement periodical unit. Theextracted observed noise sequence is information obtained through anobservation system, and is not a true state indicative of whether or notan impulsive noise is generated, or not a true power in each state.Therefore, a hidden Markovian-Gaussian noise model is applied tocalculate, from the observed noise sequence, noise characteristics in ameasurement periodical unit. Furthermore, using the extracted observednoise sequence and the calculated noise characteristics, a statesequence, indicative of whether or not a state is an impulsive noisegenerated state, is estimated in a statistical and probabilistic manner.An impulsive noise at each time point is detected from the estimatedstate sequence.

In this case, in the noise detection method of the present invention, anestimated state sequence is calculated so that the a posterioriprobability (which will be described later) of each state at each timepoint, calculable from noise characteristics, is maximized. Thus, anestimated state is accurately estimated. Further, the presence orabsence of generation of an impulsive noise at each time point isdetermined by the a posteriori probability of each of states (e.g., twostates) at each time. In the present invention, “two states”, forexample, include: a state “0” (i.e., an impulsive noise free state inwhich no impulsive noise is generated); and a state “1” (i.e., a statein which an impulsive noise is generated). Note that “0” and “1” may bereversed. A state estimated at each time point in a measurementperiodical unit is defined as either one of the two states, in which thea posteriori probability is maximized.

Moreover, in the noise detection method, the a posteriori probability iscalculated using a forward state probability and a backward stateprobability. With respect to the state at each time point, the forwardstate probability is related to a state at a preceding time point, andthe backward state probability is related to a state at a subsequenttime point. Besides, in the noise detection method of the presentinvention, the forward and backward state probabilities are identifiablefrom noise characteristics. In this case, the noise characteristics foridentifying the forward and backward state probabilities are calculatedfrom a statistic of the observed noise sequence itself.

FIG. 1 is a conceptual diagram illustrating the relationship betweenstates of Markovian noises and observed results. In FIG. 1, represents astate at each time point, and n represents an observed value at eachtime point (which is a voltage value in a power line in this case). AMarkovian noise has the following characteristic: a state s_(k+1) at atime point k+1 depends only on a state s_(k) at an immediately precedingtime point k. As illustrated in FIG. 1, an observed result n_(k) isassociated with the state s_(k), but the state s_(k) itself cannot beobtained. Accordingly, in the noise detection method of the presentinvention, noise characteristics are calculated using a hiddenMarkovian-Gaussian noise model based on an observed noise sequence.Furthermore, based on the observed noise sequence and the calculatednoise characteristics, the forward and backward state probabilities ateach time point are used, thereby calculating the a posterioriprobability of each state. From the a posteriori probability of eachstate, an estimated state matrix is calculated as described above.

FIG. 2 is a conceptual diagram conceptually illustrating a noisegeneration mechanism of a hidden Markovian noise. In FIG. 2, animpulsive noise free state In which no impulsive noise is generated isrepresented by “0”, and a state in which an impulsive noise is generatedis represented by “1”. In FIG. 2, q_(st) represents a state transitionprobability from a state s to a state t. State transition probabilitiesbetween the two states, i.e., the state “0” in which no impulsive noiseis generated and the state “1” in which an impulsive noise is generated,include the following four state transition probabilities: q₀₀; q₀₁;q₁₁; and q₁₀. Further, a noise amplitude is decided in accordance with aGaussian distribution at each time point. σ_(G) ² and σ_(I) ² in FIG. 2signify the power of a background Gaussian (G: Gaussian) noise (which isgenerated regardless of whether the state is “0” or “1”) and that of animpulse (I: Impulse) noise (which is generated only when the state is“1”), respectively. In this manner, the hidden Markovian-Gaussian noisecan be described by the four state transition probabilities and thenoise power (noise characteristics).

In the present invention, in order to detect an impulsive noise, a statesequence is estimated from the observed noise sequence n(k=1 to K)=n₁,n₂ . . . n_(k) . . . n_(K) using the foregoing noise characteristics. Inthis case, a method performed based on so-called MAP (Maximum APosteriori) estimation is used in estimating the state sequence (seeReference Document 1: R. Durbin, S. Eddy, A. Krogh, and G. Mitchison,Biological Sequence Analysis, Cambridge University Press, 1998, andReference Document 2: L. E. Baum, “An equality and associatedmaximization technique in statistical estimation for probabilisticfunctions of markov processes”, in Inequalities-III, pp. 1-8., 1972).

FIG. 3 is a trellis diagram of two-state hidden Markovian-Gaussiannoises. In FIG. 3, the horizontal axis denotes a lapse of time,representing transition of each state. In estimating a state sequence, aprobability density function of a noise in a state s in a two-statehidden Markovian-Gaussian noise channel is expressed by the followingformula 1.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 1} \right\rbrack & \; \\{{p\left( {{n_{k}s_{k}} = s} \right)} = {{g_{\sigma_{s}^{2}}\left( n_{k} \right)} = {\frac{1}{\sigma_{s}\sqrt{2\pi}}{\exp\left( {- \frac{n_{k}^{2}}{2\sigma_{s}^{2}}} \right)}}}} & (1)\end{matrix}$

(where g_(σ) ₂ (n_(k)) is a Gaussian probability density function ofaverage 0 and distribution σ²)

In this manner, the probability density function of the noise in thestate s is calculated using the distribution of power of each state,which is one of noise characteristics. Furthermore, in the Markovprocess illustrated in FIG. 2, the state of the present time pointdepends on the state at an immediately preceding time point (which isalso illustrated in FIG. 3). In other words, the state at the presenttime point is identifiable by state transition probabilities from thestate at an immediately preceding time point to the state at the presenttime point, i.e., noise characteristics. Accordingly, the state sequenceprobability of a state sequence s(1 to K+1) from a time point 1 to atime point K+1 illustrated in FIG. 3 is expressed by the followingformula 2. It should be noted that P_(s) represents a steady-stateprobability of the state s.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 2} \right\rbrack & \; \\{{P\left( s_{1}^{K + 1} \right)} = {P_{s\; 1}{\prod\limits_{k = 1}^{K}\; q_{s_{k},s_{k + 1}}}}} & (2)\end{matrix}$

(s₁ ^(K+1): State Sequence of State Sequence s(1 to K+1) until TimePoint K+1

q_(s) _(k) _(,s) _(k+1) State Transition Probability from State s_(k) atTime Point k to State s_(k+1) at Subsequent Time Point k+1)

Moreover, in the present invention, using the forward and backward stateprobabilities related to states preceding and subsequent to a state ateach: time point as illustrated in FIGS. 2 and 3, the state probabilityat each time point in the formula 2 is calculated as the a posterioriprobability. Besides, a state in which the a posteriori probability ismaximized is estimated using MAP estimation. The state estimated at eachtime point in a measurement periodical unit represents either one of thestates “0” and “1”, in which the a posteriori probability is maximized.

For each state s, a forward state probability (Forward probability)α_(k)(s) and a backward state probability (Backward probability)β_(k)(s) at the time point k are expressed by the following formulas 3and 4, respectively, as illustrated in FIG. 4. FIG. 4 providesexplanatory diagrams conceptually illustrating the forward stateprobability (Forward probability) α_(k)(s) and the backward stateprobability (Backward probability) β_(k)(s).

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 3} \right\rbrack & \; \\{{\alpha_{k}(s)} = \left\{ \begin{matrix}{{p\left( {n_{1}^{k - 1},{s_{k} = s}} \right)},} & \left( {2 \leq k \leq {K + 1}} \right) \\{P_{s},} & \left( {k = 1} \right)\end{matrix} \right.} & (3) \\{{\beta_{k}(s)} = \left\{ \begin{matrix}{{p\left( {{n_{k}^{K}s_{k}} = s} \right)},} & \left( {1 \leq k \leq K} \right) \\{1,} & \left( {k = {K + 1}} \right)\end{matrix} \right.} & (4)\end{matrix}$

As illustrated in FIGS. 3 and 4, in accordance with the trellis diagram,the forward state probability (Forward probability) α_(k)(s) at eachtime point k can be sequentially calculated from the front, and thebackward state probability (Backward probability) β_(k)(s) at each timepoint k can be sequentially calculated from the back (formulas 5 and 6).

The probability α_(k)(s) that the state becomes s at the immediatelypreceding time point k is multiplied by a probability p that theobserved result is n when the state at the time point k is s and thestate at the time point k+1 is s′, and a sum is taken, therebycalculating a forward state probability α_(k+1)(s′) that the statebecomes s′ at the time point k+1. Furthermore, the probability p isidentified by the formula 1 using the transition probability from thestate s to the state s′ and the probability density function when theobserved result is n at the time point k.

On the other hand, a probability β_(k+1)(s′) that the state becomes s′at the immediately subsequent time point k+1 is multiplied by theprobability p that the observed result is n when the state at the timepoint k is s and the state at the time point k+1 is s′, and a sum istaken, thereby calculating the backward state probability β_(k)(s) thatthe state becomes s at the time point k.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 4} \right\rbrack & \; \\\begin{matrix}{{\alpha_{k + 1}\left( s^{\prime} \right)} = {\sum\limits_{s \in {\{{0,1}\}}}\; {{\alpha_{k}(s)}{p\left( {n_{k},{s_{k + 1} = {{s^{\prime}s_{k}} = s}}} \right)}}}} \\{= {\sum\limits_{s \in {\{{0,1}\}}}{{\alpha_{k}(s)}q_{{ss}^{\prime}}{g_{\sigma_{s}^{2}}\left( n_{k} \right)}}}}\end{matrix} & (5) \\\begin{matrix}{{\beta_{k}(s)} = {\sum\limits_{s^{\prime} \in {\{{0,1}\}}}\; {{\beta_{k + 1}\left( s^{\prime} \right)}{p\left( {n_{k},{s_{k + 1} = {{s^{\prime}s_{k}} = s}}} \right)}}}} \\{= {{g_{\sigma_{s}^{2}}\left( n_{k} \right)}{\sum\limits_{s^{\prime} \in {\{{0,1}\}}}{{\beta_{k + 1}\left( s^{\prime} \right)}q_{{ss}^{\prime}}}}}}\end{matrix} & (6)\end{matrix}$

The a posteriori probability of the state s_(k) at the time point k,resulting from obtaining the observed noise sequence, is calculated bythe following formula 7 using the forward state probability (Forwardprobability) α_(k)(s) and the backward state probability (Backwardprobability) β_(k)(s) calculated by the formulas 5 and 6.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 5} \right\rbrack & \; \\{{P\left( {s_{k} = {sn_{1}^{K}}} \right)} = {\frac{P\left( {s_{k} = s} \right)}{p\left( n_{1}^{K} \right)} = \frac{{\alpha_{k}(s)}{\beta_{k}(s)}}{\sum\limits_{u \in {\{{0,1}\}}}{\alpha_{K + 1}(u)}}}} & (7)\end{matrix}$

Further, in the present invention, a state sequence s(k=1 to k+1) havingthe maximum a posteriori probability is estimated. The maximum aposteriori probability is expressed by the formula 7. The state sequenceis estimated by the following formula 8. When the estimated state is“1”, it can be assumed that impulse occurrence is observed. In thefollowing description, an estimated value is signified by a symbol(circumflex) in each mathematical expression.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 6} \right\rbrack & \; \\{{\hat{s}}_{k} = {\underset{s \in {\{{0,1}\}}}{{argmax}\;}{P\left( {s_{k} = {sn_{1}^{K}}} \right)}}} & (8)\end{matrix}$

In this case, in order to estimate a state sequence, parameters such asa state transition probability q_(ss′) and a noise power σ_(s) arenecessary. This is because the state sequence is calculated by theformula 8 that is calculated using the formulas 1 to 7. In other words,the state sequence can be estimated by calculated the respectiveparameters. It should be noted that in the following description, noisedistribution in the two states is represented by the following equation:N=[σ₀ ², σ₁ ²]^(T) (where T signifies the transposition of a matrix).Further, the four state transition probabilities q₀₀, q₀₁, q₁₁ and q₁₀for describing Markovian noise are represented by a matrix Q having eachof the state transition probabilities as an element (Q={q₀₀, q₀₁, q₁₁,q₁₀}). Furthermore, the following description will be made based on theassumption that parameters θ=(Q, N).

It should be noted that the state transition probability q_(ss′) and thenoise power σ_(s) may be expressed by using a temporal concentration ofimpulsive noises (which will hereinafter be called a “channel memory”)γ, an impulse steady-state probability (i.e., an impulsive noiseoccurrence probability in a steady state) P_(i), animpulse-to-background noise ratio R, and a background noise power σ_(G)² during a measurement periodical unit. The hidden Markovian-Gaussiannoise is also completely described by using these parameters.

In the present invention, the state sequence s(k=1 to K) estimated bythe formula 8 is estimated and calculated using the channel memory γ,impulsive noise occurrence probability P₁, impulse-to-background noiseratio R and background noise power σ_(G) ² during a measurementperiodical unit so that the a posteriori probability of the state ateach time point is maximized. In the present invention, the channelmemory γ, impulsive noise generation probability P₁,impulse-to-background noise ratio R and background noise power σ_(G) ²are calculated from the observed noise sequence. Further, the presenceor absence of generation of an impulsive noise at each time point in theobserved noise sequence is determined using these noise characteristics.

The parameters θ=(Q, N) are associated with the channel memory γ,impulsive noise occurrence probability P₁, impulse-to-background noiseratio R, and background noise power σ_(G) ² as follows.

As illustrated in FIG. 3, the noise distribution σ₀ ² when the state is“0” is represented by the following equation: σ₀ ²=σ_(G) ², and thenoise distribution σ₁ ² when the state is “1” is represented by thefollowing equation: σ₁ ²=σ_(G) ²+σ₁ ²=(1+R)σ_(G) ². In other words, theimpulse-to-background noise ratio R is R=σ_(I) ²/σ_(G) ².

Furthermore, using the state transition probabilities, the steady-stateprobability P₀ in the state “0”, i.e., in the state in which noimpulsive noise is generated, and the impulsive noise occurrenceprobability P₁ in the state “1”, i.e., in the state in which animpulsive noise is generated, are expressed as P₀=q₁₀/(q₀₁+q₁₀) andP₁=q₀₁(q₀₁+q₁₀), respectively. In this case, the probability isexpressed as follows: Matrix P=[P₀P₁]^(T). It should be noted that dueto the rarity of an impulsive noise, it can be assumed that P₁<½.

Average durations T₀ and T₁ of the states “0” and “1” are given byT₀=1/q₁₀ and T₁=1/q₀₁, respectively. The channel memory γ is defined byγ=1/(q₀₁+q₁₀). In other words, the channel memory is the reciprocal of asum of probabilities of transitions between the different states s andt, and serves as an indicator of a continuous period of the same state.The average durations T₀ and T₁ of the respective states “0” and “1” areboth greater than 1 when the channel memory γ is γ<1, and the memory isdivergent. The state in which the average durations T₀ and T₁ are bothgreater than 1 does not apply to the noise model of impulsive noises.Accordingly, it can be assumed that the channel memory γ for describingimpulsive noises is γ>1. When the channel memory γ is γ=1, the channelbecomes memoryless, and which of the states “0” and “1” will occur iscompletely randomized. The noise in this case will be called a“Bernoulli-Gaussian noise”.

The closer the value of the channel memory γ to 1 (γ>1), the more likelyit is that a period of generation of a random noise can be expressed.Further, the higher the value of the channel memory γ than 1 and thegreater the numerical value, the more likely it is that concentrativenoise generation is recognized. FIG. 5 provides waveform diagrams eachillustrating a hidden Markovian-Gaussian noise amplitude with respect tothe channel memory. In each of the waveform diagrams, the impulsivenoise occurrence probability P₁=0.1, the impulse-to-background noiseratio R=100, and the background noise power σ_(G) ²=1. The channelmemory γ=1 in the upper waveform diagram, the channel memory γ=3 in themiddle waveform diagram, and the channel memory γ=10 in the lowerwaveform diagram. As illustrated in FIG. 5, the temporal concentrationof impulsive noises can be expressed using the value of the channelmemory. An impulsive noise appears sporadically when the channel memoryγ=1, but impulsive noises concentratedly appears when the channel memoryγ=10. Whether or not an impulsive noise is generated is decided based ona state sequence. However, since the state sequence cannot be found, thestate sequence has to be appropriately estimated in order to detect animpulsive noise.

It should be noted that the presence or absence of generation ofimpulsive noises for each measurement periodical unit may also bedetermined from the noise characteristics. For example, of the fourstate transition probabilities, when the transition probabilities q₀₁and q₁₀ between the states averagely different during a measurementperiodical unit are high, i.e., when the value of the channel memory γis low, there is a high possibility that one of the states does notoccur concentratedly and random noises are generated. When the power σ₁² of the state in which impulsive noises are generated is higher thanthe power σ₀ ² of the state in which no impulsive noise is generated,i.e., when the value of the impulse-to-background noise ratio R is high,there is a high possibility that impulsive noises with a high amplitudeis observed. As described above, in addition to the presence or absenceof an impulsive noise at each time point, the presence or absence ofimpulsive noises during the entire measurement periodical unit can bedetermined macroscopically. By setting the measurement periodical unitas a period concerning the cycle of communication between communicationapparatuses, the influence of impulsive noises on a communicationtrouble during this period may be taken into consideration. Moreover, amacroscopic determination is made for each measurement periodical unit;thus, each state sequence may be estimated for only a period duringwhich impulsive noises are generated, and an impulsive noise at eachtime point may be detected in details, thereby making it possible tosimplify processing.

The association between the channel memory γ, impulsive noise occurrenceprobability P₁, impulse-to-background noise ratio R and background noisepower σ_(G) ², and the state transition probability matrix Q and averagenoise power N is summarized as follows.

Channel Memory γ: γ=1/(q₀₁+q₁₀)

Impulsive noise occurrence probability P₁: P₁=q₀₁/(q₀₁+q₁₀)

Impulse-To-Background Noise Ratio R:R=σ_(I) ²/σ_(G) ²=(σ₁ ²−σ₀ ²)/σ₀ ²

Background Noise Power σ_(G) ²: σ_(G) ²=σ₀ ²

As described above, the state sequence can be estimated by the formula8. The formula 8 is calculated using the formulas 1 to 7 based on thenoise characteristics (the state transition probability matrix Q andaverage noise power N, or the channel memory γ, impulsive noiseoccurrence probability P₁, impulse-to-background noise ratio R andbackground noise power σ_(G) ²). However, it is necessary to accuratelyestimate the state sequence in order to accurately detect an impulsivenoise. For this purpose, it is further necessary to obtain noisecharacteristics for increasing the likelihood of the observed noisesequence. In the present invention, the noise characteristics areestimated based on a BW algorithm, which is a kind of EM(Expectation-maximization) algorithms, as follows.

Specifically, first, initial values of noise characteristics, i.e.,initial values of state transition probability and average noise powercalculated from an observed noise sequence, are decided. Using thedecided initial values and the foregoing formulas 1 to 6, a forwardstate probability indicative of a two-state probability based on apreceding state at each time point in the obtained observed noisesequence, and a backward state probability indicative of a two-stateprobability based on a state at a subsequent time point are calculated.Then, using the forward and backward state probabilities, the statetransition probability and noise power for the entire observed noisesequence are further calculated. In order to detect an impulsive noise,the calculation of the forward and backward state probabilities, and thecalculation of the state transition probability and noise power arerepeated. Thus, the state transition probability and noise power formaximization of the likelihood of the entire observed noise sequence(i.e., maximization expected values of the state transition probabilityand noise power) are calculated. From the state transition probabilityand noise power calculated in the course of repetition of updating, theforward and backward state probabilities at each time point arecalculated, and from these forward and backward state probabilities, thea posteriori probability of each state at each time point is calculated(formulas 3 and 4). Thus, a state sequence in which the a posterioriprobability of the state at each time point is likely to be maximizedwill be estimated. Detailed description will be made below.

When the a posteriori probability of a pair of states (states s and s′)at time points k and k+1, i.e., an observed noise sequence n(k=1 to K),has been obtained, the probability of transition from the state s at thetime point k to the state s′ at the time point k+1 is given by thefollowing formula 9.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 7} \right\rbrack & \; \\{{P\left( {{s_{k} = s},{s_{k + 1} = {s^{\prime}n_{1}^{K}}}} \right)} = \frac{{\alpha_{k}(s)}q_{{ss}^{\prime}}{g_{\sigma_{s}^{2}}\left( n_{k} \right)}{\beta_{k + 1}\left( s^{\prime} \right)}}{\sum\limits_{u \in {\{{0,1}\}}}\; {\alpha_{K + 1}(u)}}} & (9)\end{matrix}$

Using the formulas 7 and 9, estimated values of the state transitionprobability and noise power in the state sequence s(k=1 to K+1) arecalculated as follows by the following formulas 10 and 11, respectively.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 8} \right\rbrack & \; \\{\left. {\hat{q}}_{{ss}^{\prime}}\leftarrow\frac{\sum\limits_{k = 1}^{K}\; {{\alpha_{k}(s)}{\beta_{k + 1}\left( s^{\prime} \right)}{g_{{\hat{\sigma}}_{s}^{2}}\left( n_{k} \right)}}}{\sum\limits_{k = 1}^{K}\; {{\alpha_{k}(s)}{\beta_{k}(s)}}} \right.\left. {\hat{q}}_{{ss}^{\prime}}\leftarrow\frac{\sum\limits_{k = 1}^{K}\; {P\left( {{s_{k} = s},{s_{k + 1} = {s^{\prime}n_{1}^{K}}}} \right)}}{\sum\limits_{k = 1}^{K}{P\left( {s_{k} = {sn_{1}^{K}}} \right)}} \right.} & (10) \\{{\left. {\hat{\sigma}}_{s}^{2}\leftarrow\frac{\sum\limits_{k = 1}^{K}\; {{\alpha_{k}(s)}{\beta_{k}(s)}n_{k}^{2}}}{\sum\limits_{k = 1}^{K}\; {{\alpha_{k}(s)}{\beta_{k}(s)}}} \right. = \frac{\sum\limits_{k = 1}^{K}{{P\left( {s_{k} = {sn_{1}^{K}}} \right)}n_{k}^{2}}}{\sum\limits_{k = 1}^{K}{P\left( {s_{k} = {sn_{1}^{K}}} \right)}}}{{\hat{q}}_{{ss}^{\prime}}\text{:}{Estimated}\mspace{14mu} {Value}\mspace{14mu} {of}\mspace{14mu} {State}\mspace{14mu} {Transition}\mspace{14mu} {Probability}}{{\hat{\sigma}}_{s}^{2}\text{:}{Estimated}\mspace{14mu} {Value}\mspace{14mu} {of}\mspace{14mu} {Noise}\mspace{14mu} {Power}}} & (11)\end{matrix}$

In the present invention, the initial values (θ) of the state transitionprobability and noise power are decided for the extracted observed noisesequence n(k=1 to K), and the calculations of the formulas 5, 6, 10 and11 are repeated. In the course of the repetition, when an increase in alogarithmic likelihood becomes lower than a prescribed threshold or whenthe number of the calculations exceeds a given number L, the calculationof the state transition probability and average noise power, i.e.,updating, is stopped (formula 12).

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 9} \right\rbrack & \; \\\left. \begin{matrix}{{\Delta \; \ln \; {p\left( {n_{1}^{K}\hat{\theta}} \right)}} > {{Threshold}\Delta}} \\{or} \\{{{Number}\mspace{14mu} {of}\mspace{14mu} {Calculations}\mspace{14mu} l} \geq L}\end{matrix} \right\} & (12)\end{matrix}$

As described above, in the present invention, the state at each timepoint is reproduced based on the a posteriori probability using the BWalgorithm and MAP estimation, and the state transition probability andnoise power indicative of the noise characteristics during a measurementperiodical unit are accurately calculated; furthermore, using the statetransition probability q_(ss′) and noise power σ_(s) ² calculated as thevalues that maximize the likelihood of the observed noise sequence, thestate sequence of the state at each time point can be accuratelyestimated by the foregoing formulas 5 to 8. Accordingly, the accuracy ofdetection of an impulsive noise at each time point is increased.

Moreover, in the present invention, a moment method is used in decidinginitial values in the foregoing BW algorithm (see Reference Document 3:K. Fukunaga and T. E. Flick, “Estimation of the parameters of a Gaussianmixture using the method of moments”, IEEE Trans. Pattern Anal. Mach.Intell., vol. PAMI-5, no. 4, pp. 410-416, July 1983). This is becausesince there are a large number of local solutions, the logarithmiclikelihood that the calculation is stopped in the present invention isgreatly influenced by the initial values. In the present invention,since the initial values are decided by the detection apparatus based astatistic of the observed noise sequence itself, the possibility ofavoiding convergence to an erroneous local solution is increased, andthe need for a step given by human is eliminated to enable theautomation of the detection. Thus, the influence of information set byhuman in the system can be eliminated to the extent possible.

Specifically, in a method for deciding the initial values, the initialvalues of the state transition probability and noise power in the BWalgorithm according to the present invention, i.e., the initial value ofthe matrix Q and the initial value of N, are decided by using threemoments in the moment method from the obtained observed noise sequence.In the noise detection method of the present invention, the threemoments are calculated, and the noise power and a threshold for anamplitude value of the observed noise sequence are calculated using thecalculated moments. Thus, the calculated noise power is decided as theinitial value of the noise power N, and an estimated state sequence iscalculated using the calculated threshold to decide the initial value ofthe state transition probability matrix Q. Detailed description will bemade below.

The estimated value N of the noise power (average noise power) in thestate sequence s(k=1 to K) is estimated as follows. First, theprobability distribution of the observed noise sequence n(k=1 to K) of atwo-state hidden Markovian-Gaussian noise is given as a mixture Gaussiandistribution as expressed in the following formula 13.

[Exp. 10]

gm _(N)(n _(k))=P ₀ g _(σ) ₀ ₂(n _(k))+P ₁ g _(σ) ₁ ₂(n _(k))  (13)

Since the Gaussian distribution is given as expressed in the formula 13,the moment method is used to perform: a maximum likelihood estimation ofthe steady-state probability P₀ of the state “0” and steady-stateprobability P₁ of the state “1”, i.e., the matrix P (=[P₀P₁]^(T)); and amaximum likelihood estimation of the distribution of noises in k=1 to K,which is noise power in each state, i.e., the matrix N (=[σ₀ ², σ₁²]^(T)).

In the moment method, three moments a, b and c of the following formula14 are calculated from the observed noise sequence n(k=1 to K).

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 11} \right\rbrack & \; \\{{{a = {\sqrt{\frac{\pi}{2}}{E\left\lbrack {n_{k}} \right\rbrack}}},{b = {E\left\lbrack n_{k}^{2} \right\rbrack}},{c = {\frac{\sqrt{2\pi}}{4}{E\left\lbrack {n_{k}}^{3} \right\rbrack}}}}{E\text{:}{Sample}\mspace{14mu} {Mean}\mspace{14mu} {of}\mspace{14mu} {Sequence}}} & (14)\end{matrix}$

From the three moments calculated by the formula 14, the estimatedvalues of standard deviations of noises in each state can be calculatedby the following formulas 15 and 16.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 12} \right\rbrack & \; \\{{\hat{\sigma}}_{0} = \frac{{ab} - c + \sqrt{\left( {{ab} - c} \right)^{2} - {4\left( {a^{2} - b} \right)\left( {b^{2} - {ac}} \right)}}}{2\left( {a^{2} - b} \right)}} & (15) \\{{\hat{\sigma}}_{1} = \frac{{ab} - c - \sqrt{\left( {{ab} - c} \right)^{2} - {4\left( {a^{2} - b} \right)\left( {b^{2} - {ac}} \right)}}}{2\left( {a^{2} - b} \right)}} & (16)\end{matrix}$

The initial estimated value of the noise power of the formula 11 can bedecided by using the formulas 15 and 16.

It should be noted that the estimated value of N (=[σ₀ ², σ₁ ²]^(T)) andthe impulse-to-background noise ratio R can be identified by using theestimated values of the noise standard deviations, which are calculatedby the formulas 15 and 16. Furthermore, the estimated value of theimpulsive noise occurrence probability P₁ is calculated as expressed inthe following formula 17.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 13} \right\rbrack & \; \\{{\hat{P}}_{1} = {\frac{1}{2}\left( {1 - \frac{{2\; a^{3}} - {3\; {ab}} + c}{\sqrt{\left( {{ab} - c} \right)^{2} - {4\left( {a^{2} - b} \right)\left( {b^{2} - {ac}} \right)}}}} \right)}} & (17)\end{matrix}$

When the value of the steady-state probability, that is impulsive noiseoccurrence probability P₁ of the state “1”, calculated by the formula17, is equal to or lower than 0, or is equal to or higher than 0.5 (itis assumed that P₁<½ due to the rarity of an impulsive noise), it can bedetermined that a white Gaussian noise (background Gaussian noise) isincluded in the extracted observed noise sequence but no impulsive noiseis included therein. Accordingly, in the present invention, it is firstdetermined whether or not the impulsive noise occurrence probability P₁falls within the range of 0<P₁<½, and when the impulsive noiseoccurrence probability P₁ does not fall within this range, it isdetermined that only a white Gaussian noise is included in the extractedobserved noise sequence, thus making it unnecessary to forcedly applyingthe observed noise sequence to an impulsive noise model in performingdetection.

Further, when a sequence length of the observed noise sequence is large,i.e., when the number of measurements of voltage values as observedresults is sufficiently large, the impulsive noise occurrenceprobability P₁ derived by the moment method based on the formulas 15 to17 and the impulsive-to-background noise ratio R calculated using thenoise standard deviations derived by the formulas 16 and 17 can beaccurately estimated.

Furthermore, the initial value of the state transition probabilitymatrix Q is also decided by obtaining the estimated state sequence s(k=1to K) using a threshold A that is based on the average noise powercalculated by the noise standard deviations (formulas 15 and 16) givenfrom the foregoing three moments. The threshold A is given as expressedin the following formula 18. As expressed in the formula 18, in theestimated state sequence s, the state s_(k) is “0” when the k-th sample(voltage value) n_(k) in the obtained observed noise sequence is equalto or lower than the threshold A, and the state s_(k) is “1” when thek-th sample n_(k) (voltage value) exceeds the threshold Λ.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 14} \right\rbrack & \; \\{{\hat{s}}_{k} = \left\{ {{\begin{matrix}{0,} & {\left( {n_{k} \leq \Lambda} \right),} \\{1,} & {\left( {n_{k} > \Lambda} \right),}\end{matrix}\mspace{14mu} \Lambda} = \sqrt{\frac{{\hat{\sigma}}_{0}^{2}{\hat{\sigma}}_{1}^{2}}{{\hat{\sigma}}_{1}^{2} - {\hat{\sigma}}_{0}^{2}}{\ln \left( \frac{{\hat{\sigma}}_{1}^{2}}{{\hat{\sigma}}_{0}^{2}} \right)}}} \right.} & (18)\end{matrix}$

From the estimated value of the state sequence s(k=1 to K) estimated bythe formula 18, the initial value of the state transition probabilitymatrix Q between the respective states is calculated. Specifically, fromthe number A_(ss′), of transitions from the state s to the state s′ inthe estimated state sequence s(k=1 to K), and the number A_(s) of thestates s in s(k=1 to K−1) in the estimated state sequence s(k=1 to K),an initial value q_(ss′) of the state transition probability from thestate s to the state s′ is calculated by the following formula:

q _(ss′) =A _(ss′) /A _(s)  (19)

In the present invention, the average noise power and state transitionprobability for maximization of the likelihood of the observed noisesequence are calculated by the BW algorithm using the initial value ofthe average noise power derived from the three moments based on themoment method for the observed noise sequence, and the initial value ofthe state transition probability as mentioned above. The presence orabsence of generation of an impulsive noise may be determined from theestimated state sequence using only the moment method. In the momentmethod, as mentioned above, the estimated state sequence is calculatedby only comparisons made between: the threshold Λ calculated from themoments; and the voltage values at the respective time points. However,when determinations are made using only the threshold, the accuracy isinsufficient since a single impulsive noise is detected in a subdividedmanner, for example. It is to be noted that the accuracy is enough todecide the initial values of the BW algorithm, subjective detectionconditions such as an initial value given by human and a threshold bywhich an initial value is decided can be eliminated, and a step ofgiving an initial value by human is unnecessary, thus enablingautomation. Using the initial values calculated in the above-describedmanner, the average noise power and state transition probability formaximization of the likelihood of the observed noise sequence in thepresent invention are calculated; hence, the accuracy of the estimatedstate sequence calculated from the noise characteristics is alsoincreased, thus allowing impulsive noises to be automatically detectedwith higher accuracy.

It should be noted that in calculating the average noise power and statetransition probability for maximization of the likelihood of theobserved noise sequence by the foregoing BW algorithm, and in estimatingthe state sequence, an impulsive noise is preferably extracted byeliminating components other than the impulsive noise. When the statesequence is obtained from the observed noise sequence, in which noimpulsive noise is generated, based on the assumption that an impulsivenoise is included, a white Gaussian noise might still be forcedly anderroneously analyzed as part of an impulsive noise. Therefore, whetheror not an impulsive noise is generated in the observed noise sequence isdetermined using a statistical information criterion, and when noimpulsive noise is generated, the estimation of the state transitionprobability and noise power performed based on the foregoing BWalgorithm and MAP estimation is skipped. A noise generated in themeasurement period in this case may be substantially determined as awhite Gaussian noise. Thus, the accuracy of detection of an impulsivenoise is further increased.

It should be noted that as the above-mentioned information criterion, alogarithmic likelihood, TIC (Takeuchi Information Criterion [seeReference Document 4]), AIC (Akaike Information Criterion [see ReferenceDocument 5]), or a criterion provided by focusing attention on acorrection term of TIC or AIC, i.e., the number of free parameters, isused in addition to the value of the foregoing impulsive noiseoccurrence probability P₁. A plurality of these criteria may be used incombination.

(Reference Document 4: M. Stone, “An asymptotic equivalence of choice ofmodel by cross-validation and Akaike's criterion”, J. Roy. Statist.Soc., vol. 39, pp. 44-47, 1977)

(Reference Document 5: H. Akaike, “A new look at the statistical modelidentification”, IEEE Trans. Autom. Control, vol. AC-19, no. 6, pp.716-723, December 1974)

In particular, the criterion provided by the number of free parametersis capable of increasing the detection accuracy by determining whetheror not an impulsive noise is included based on whether or not thefollowing formula 20 is satisfied.

In addition, it is expected that a value close to 1 is derived as thecorrection term of AIC at left-hand side of formula 20 when theamplitude probability distribution of the observed noise sequence is aGaussian distribution, and a value greater than 3 is derived as thecorrection term of AIC when the amplitude probability distribution ofthe observed noise sequence is a mixture Gaussian distribution.Therefore the right-hand value of formula 20 is assumed to be a value 2,that is z value is 1, thereby making it possible to determine whether ornot the amplitude probability distribution of the observed noisesequence is a mixture Gaussian distribution, means includes impulsivenoises. However, the right-hand value of formula 32 should not to befixed to value 2, so that z value is fine-tuned from 1.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 15} \right\rbrack & \; \\{{{\frac{E_{K}\left\lfloor n_{k}^{4} \right\rfloor}{2\; K\; {\hat{\sigma}}^{4}} - \frac{1}{2}} > {1 + z}}{{\hat{\sigma}}^{2}\text{:}{Two}\text{-}{State}\mspace{14mu} {Weighted}\mspace{14mu} {Distribution}\mspace{14mu} \left( {= {{{\hat{P}}_{0}{\hat{\sigma}}_{0}^{2}} + {{\hat{P}}_{1}{\hat{\sigma}}_{1}^{2}}}} \right)}{{z\text{:}{Any}\mspace{14mu} {value}\mspace{14mu} {meets}\mspace{14mu} z} > 0}} & (20)\end{matrix}$

It should be noted that in the present invention, a predeterminedmeasurement periodical unit is a periodical unit concerning thecommunication method between communication apparatuses connected to thepower line. In other words, a communication parameter of a physicallayer in the communication is set as a changeable unit, thereby makingit possible to favorably conduct evaluations for determining whether ornot the communication parameter needs to be changed in accordance with afeature of impulsive noises in each period. For example, it ispreferable to use a communication cycle and/or a frame length in acommunication protocol as the measurement periodical unit. Also whenTDMA (Time Division Multiple Access) is adopted as a communicationmethod, a communication cycle, a frame length, a slot length, etc., maybe similarly used as the measurement periodical unit.

In the present invention, for example, communication is carried out inaccordance with FlexRay (registered trademark). Accordingly, thepredetermined measurement periodical unit is defined as a communicationcycle serving as the unit of media access. Thus, an optimalcommunication parameter in FlexRay can be selected.

It should be noted that in obtaining the estimated state sequence inorder to detect, whether or not an impulsive noise is generated, animpulsive noise is not necessarily detected for each predeterminedinterval during which signal levels are sampled. When one or a pluralityof the predetermined intervals corresponds/correspond to single bitinformation in digital information, the presence or absence of animpulsive noise may be detected for each section using a plurality ofthe predetermined intervals as a single section. Thus, the presence orabsence of generation of an impulsive noise at a time pointcorresponding to each bit can be determined. It can be estimated that indigital information received through communication, there might be anerror in a bit at a time point at which an impulsive noise is generated.

In the present invention, a simulation of an impulsive noise is alsoenabled. Based on observed noise sequences in which signal levels in acommunication medium of a communication system in a plurality ofdifferent known situations are measured at a predetermined interval onthe time series, noise characteristics associated with the respectivesituations are calculated using a hidden Markovian-Gaussian noise modelas described above. In accordance with each situation, an impulsivenoise is detected from the observed noise sequence and the calculatednoise characteristics by the foregoing detection method, and a frequencyof the detected impulsive noise is calculated. Furthermore, inassociation with each situation, the noise characteristics and thecalculated impulsive noise frequency are recorded. A plurality ofdifferent known situations refer to connection configuration patterns ofthe communication system. For example, when the communication system isan in-vehicle PLC system, an observed noise sequence is extracted foreach of situations including: the type of an actuator connected to apower line, e.g., whether the actuator is one used for a door lock orone used for a mirror; whether the actuator has started its operation;whether the actuator has stopped its operation; and a change in thelength of the power line, for example, to its end. And the noisecharacteristics and impulsive noise frequency for each measurementperiodical unit are calculated from each of the observed noisesequences. Further, a state sequence is generated by the hiddenMarkovian-Gaussian noise model using the noise characteristicsassociated with each situation, which have been calculated in advance inaccordance with the configuration of the communication system to besimulated (formulas 5 to 8). When the communication system is anin-vehicle PLC system, the state sequence is generated in accordancewith the length of the power line, the numbers and types of theconnected communication apparatuses and actuators, etc. Then, apseudonoise is generated from the generated state sequence and theimpulsive noise frequency.

The execution of the simulation according to the present inventionallows detailed preliminary studies to be conducted, for example, on acommunication method effective for the noise characteristics of animpulsive noise and the impulsive noise frequency, which are estimatedautomatically from statistical properties of the observed noisesequences. Thus, an efficient simulation can be realized.

Furthermore, in the present invention, the selection of an optimalcommunication method or the like from preset methods may be automatedusing a computer. In the present invention, based on observed noisesequences in which voltage values in a power line of an in-vehicle PLCsystem in a plurality of different known situations are measured at apredetermined interval on the time series, noise characteristicscalculated using a hidden Markov model and associated with eachsituation, and the frequency of an impulsive noise detected in theabove-described manner are recorded. In this case, the noisecharacteristics are information calculated automatically based on theobserved noise sequence so as to avoid a situation where an erroneouslocal solution is calculated. In addition, in the computer, based on acircuit configuration to be designed, an estimated state sequence of ahidden Markov model is estimated and calculated using the noisecharacteristics of the respective situations calculated and recorded inadvance, and a pseudonoise is generated using this estimated statesequence and the impulsive noise frequency. Moreover, a communicationmethod, a communication frequency and a communication parameter, eachserving as a candidate in the PLC system to be designed, are received,and a simulation of communication error occurrence (communicationsimulation) is performed for each of these candidates by using thegenerated pseudonoise, thereby identifying optimal candidates based on acommunication error probability derived from simulation results.

In the present invention, based on the observed noise sequence derivedby observation in each situation, and on the noise characteristics andfrequency of an impulsive noise estimated automatically from statisticalproperties of the observed noise sequence itself, a pseudonoiseresponsive to the situation is generated, and a simulation is executedusing this pseudonoise. Therefore, detailed preliminary studies can beconducted, for example, on a communication method or the like effectivefor an impulsive noise that appears depending on a wide variety ofvehicle types or options, for example.

Further, in the present invention, a communication system may beconfigured to include an optimization apparatus for optimizing acommunication method, a communication frequency and/or a parameter. Theoptimization apparatus obtains an observed noise sequence of signallevels in a communication medium, obtains noise characteristics using ahidden Markovian-Gaussian noise model based on the obtained observednoise sequence, and obtains noise features such as frequencies from thecalculated noise characteristics. The obtaining the observed noisesequence and noise features is preferably sequentially carried out so asto be continuously updated. Furthermore, the optimization apparatussequentially decides the optimal communication method, frequency andparameter based on comparisons made between: a plurality ofcommunication methods, communication frequencies and communicationparameters recorded in advance as candidates; and the noise features.

Thus, based on the features of an impulsive noise detected automaticallyusing statistical properties of noises that are actually generated, thecommunication method, frequency and parameter, which minimize theinfluence of the impulsive noise, can be suitably selected.

Besides, in the present invention, a transmitter of a communicationapparatus in a communication system may include a means for adjusting acarrier wave frequency, and a preceding stage of a limiter of a receivermay include a band rejection filter capable of adjusting a band, thusavoiding the frequency of an impulsive noise detected by the foregoingimpulsive noise detection method. The communication system includes ananalysis apparatus, for example, and the analysis apparatus is allowedto read frequencies calculated in advance in accordance with a pluralityof different known situations. The analysis apparatus adjusts thefrequency of the carrier wave of the transmitter and that of the bandrejection filter preceding the receiver so that the frequency of animpulsive noise is avoided in accordance with the current situation ofthe communication system. Thus, the communication system is capable ofperforming communication in accordance with each situation without beinginfluenced by the impulsive noise.

It should be noted that the analysis apparatus is not limited to aconfiguration in which adjustments are made so as to avoid impulsivenoise frequencies stored in advance in accordance with the knownsituations. Furthermore, the analysis apparatus may be configured todetect an impulsive noise in real time, obtain the frequency thereof,and adjust the carrier wave frequency of the transmitter-receiver andthe frequency of the band rejection filter so as to avoid the calculatedfrequency.

In the case of the present invention, a hidden Markovian-Gaussian noisemodel is applied in accordance with characteristics of a suddenimpulsive noise generated in an event-driven manner in a communicationmedium of the communication system, thereby enabling automatic andhigh-accuracy impulsive noise detection that has been conventionallydifficult.

In particular, when the communication system is a PLC system, there is ahigh possibility that a sudden impulsive noise is generated becausevarious devices are connected to a power line serving as a communicationmedium, and therefore, more favorable communication is enabled bydetecting the impulsive noise with high accuracy.

In particular, when the PLC system is an in-vehicle PLC system, there isa high possibility that received and transmitted information isinformation important for maintaining safety. High-accuracy detection ofan impulsive noise that is suddenly generated, and execution ofcommunication in which a period of generation of the impulsive noise isavoided are very useful when the PLC system is an in-vehicle PLC system.

Further, since an impulsive noise generated in each situation in thecommunication system can be automatically modeled with high accuracy, anoise simulation can be achieved with high accuracy at a design stage ofthe communication system, thereby making it possible to implement thecommunication system that uses an optimal frequency for effectivelyavoiding impulsive noises. In addition to the frequency, a communicationmethod, a communication frequency, and other communication parametersmay be selected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual diagram illustrating the relationship betweenstates of Markovian noises and observed results.

FIG. 2 is a conceptual diagram conceptually illustrating a noisegeneration mechanism of a hidden Markovian noise.

FIG. 3 is a trellis diagram of two-state hidden Markovian-Gaussiannoises.

FIG. 4 provides explanatory diagrams conceptually illustrating a forwardstate probability (Forward probability) α_(k)(s) and a backward stateprobability (Backward probability) β_(k)(s).

FIG. 5 provides waveform diagrams each illustrating a hiddenMarkovian-Gaussian noise amplitude with respect to a channel memory.

FIG. 6 is a block diagram illustrating a configuration of an in-vehiclePLC system according to Embodiment 1.

FIG. 7 is a block diagram illustrating internal configurations of an ECUand a noise detection apparatus, which are included in the in-vehiclePLC system according to Embodiment 1.

FIG. 8 is a functional block diagram illustrating functions implementedby the noise detection apparatus included in the in-vehicle PLC systemaccording to Embodiment 1.

FIG. 9 is a flow chart illustrating an example of processing executed bythe noise detection apparatus according to Embodiment 1.

FIG. 10 is a flow chart illustrating an example of a procedure ofprocessing for calculating parameters θ (noise characteristics) formaximization of the likelihood of an observed noise sequence based on aBW algorithm using a moment method, executed by functions of a parameterestimation section of the noise detection apparatus according toEmbodiment 1.

FIG. 11 is a graph illustrating a detection error probability caused bythe noise detection apparatus according to Embodiment 1.

FIG. 12 is a graph illustrating a detection error probability caused bythe noise detection apparatus according to Embodiment 1.

FIG. 13 is a graph illustrating a detection error probability caused bythe noise detection apparatus according to Embodiment 1.

FIG. 14 is a waveform diagram illustrating impulsive noise detectionresults obtained by the noise detection apparatus according toEmbodiment 1.

FIG. 15 is an explanatory diagram illustrating examples of noisecharacteristics of an impulsive noise detected by the noise detectionapparatus according to Embodiment 1.

FIG. 16 is a functional block diagram illustrating functions implementedby a noise detection apparatus according to Embodiment 2.

FIG. 17 is a flow chart illustrating an example of processing executedby the noise detection apparatus according to Embodiment 2.

FIG. 18 is a flow chart illustrating details of processing forcalculating initial values of noise power and steady-state probabilityusing a moment method by the noise detection apparatus according toEmbodiment 2.

FIG. 19 is a flow chart illustrating details of processing forcalculating four state transition probabilities by the noise detectionapparatus according to Embodiment 2.

FIG. 20 is a flow chart illustrating an example of a procedure ofprocessing for calculating parameters θ (noise characteristics) formaximization of the likelihood of an observed noise sequence based on aBW algorithm by the noise detection apparatus according to Embodiment 2.

FIG. 21 is a graph illustrating detection error probabilities caused bythe noise detection apparatus according to Embodiment 2.

FIG. 22 is a graph illustrating detection error probabilities caused bythe noise detection apparatus according to Embodiment 2.

FIG. 23 is a graph illustrating detection error probabilities caused bythe noise detection apparatus according to Embodiment 2.

FIG. 24 is a block diagram illustrating a configuration of a simulationapparatus according to Embodiment 3.

FIG. 25 is an explanatory diagram illustrating exemplary details of anoise record stored in a storage section of the simulation apparatusaccording to Embodiment 3.

FIG. 26 is a flow chart illustrating an example of a procedure ofprocessing executed by the simulation apparatus according to Embodiment3.

FIG. 27 is a block diagram illustrating a configuration of an in-vehiclePLC design apparatus according to Embodiment 4.

FIG. 28 is a flow chart illustrating an example of a procedure ofprocessing executed by the in-vehicle PLC design apparatus according toEmbodiment 4.

FIG. 29 is a block diagram illustrating a configuration of an in-vehiclePLC system according to Embodiment 5.

FIG. 30 is a block diagram illustrating an internal configuration of anoptimization apparatus included in the in-vehicle PLC system accordingto Embodiment 5.

FIG. 31 is a functional block diagram illustrating functions implementedby the optimization apparatus included in the in-vehicle PLC systemaccording to Embodiment 5.

FIG. 32 is a flow chart illustrating an example of a procedure ofprocessing executed by the optimization apparatus according toEmbodiment 5.

FIG. 33 is a block diagram illustrating a configuration of an in-vehiclePLC system according to Embodiment 6.

FIG. 34 is a block diagram illustrating an internal configuration of afilter section included in the in-vehicle PLC system according toEmbodiment 6.

FIG. 35 is a block diagram illustrating an internal configuration of ananalysis apparatus included in the in-vehicle PLC system according toEmbodiment 6.

FIG. 36 is a functional block diagram illustrating functions implementedby the analysis apparatus included in the in-vehicle PLC systemaccording to Embodiment 6.

FIG. 37 is a flow chart illustrating an example of a procedure ofprocessing executed by the analysis apparatus according to Embodiment 6.

FIG. 38 is a waveform diagram illustrating an example of impulsivenoises generated in a power line.

FIG. 39 is a waveform diagram illustrating an example of an impulsivenoise generated in a power line.

FIG. 40 is a waveform diagram illustrating an example of an impulsivenoise generated in a power line.

EXPLANATION OF ITEM NUMBERS

-   1 ECU (communication apparatus)-   2 actuator (device)-   3 power line-   6 noise detection apparatus-   601 parameter estimation section-   602 initial value deciding section-   603 BW algorithm calculation section-   604 parameter output section-   605 impulsive noise detection section-   64 measurement section-   7 simulation apparatus-   70 control section-   71 storage section-   73 noise record-   75 condition input section-   76 pseudonoise generation section-   8 in-vehicle PLC design apparatus-   80 control section-   81 storage section-   83 noise record-   84 communication condition candidate group-   86 input/output section-   87 pseudonoise generation section-   88 communication simulation execution section-   9 optimization apparatus-   94 measurement section-   95 impulsive noise feature-   96 communication condition candidate group-   901 parameter estimation section-   905 impulsive noise detection section-   906 impulsive noise feature calculation section-   907 optimal candidate deciding section-   100 analysis apparatus-   101 control section-   105 measurement section-   106 adjustment section-   107 impulsive noise frequency information-   1001 parameter estimation section-   1005 impulsive noise detection section-   1006 impulsive noise frequency calculation section-   21 band rejection filter

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, the present invention will be specifically described withreference to the drawings illustrating embodiments of the presentinvention.

It should be noted that the following embodiments will be describedbased on an example in which the present invention is applied to anin-vehicle PLC system that realizes, via PLC, communication between ECUsinstalled on a vehicle.

Embodiment 1

FIG. 6 is a block diagram illustrating a configuration of an in-vehiclePLC system according to Embodiment 1. The in-Vehicle PLC systemincludes: a plurality of ECUs 1, 1, . . . ; actuators 2, 2, . . .operated in response to control data transmitted from the ECUs 1, 1, . .. ; power lines 3, 3, . . . through which electric power is supplied toeach of the ECUs 1, 1, . . . and the actuators 2, 2, . . . ; a battery 4for supplying electric power to respective devices through the powerlines 3, 3, . . . ; a junction box 5 for branching and junction of thepower lines 3, 3, . . . ; and a noise detection apparatus 6 fordetecting a noise in each power line 3.

As illustrated in FIG. 6, in Embodiment 1, the ECUs 1, 1, . . . and theactuators 2, 2, . . . make bus-type connections to the power line 3. Aconnection topology may be a star-type connection, or may be a combinedtype in which a bus-type connection and a star-type connection arecombined.

The battery 4 is charged with electricity by an unillustrated alternatorthat generates electric power from an engine. The battery 4 is connectedat its one end (negative terminal) to a ground, and is connected at itsother end (positive terminal) to the junction box 5 via the power line3. The battery 4 supplies a driving voltage of 12 V, for example, toeach device.

The junction box 5 includes a branching and junction circuit for thepower line 3. A plurality of the power lines 3, 3, . . . are connectedto and branched from the junction box 5. The plurality of power lines 3,3, . . . are connected to the associated ECUs 1, 1, . . . and actuators2, 2, . . . . The junction box 5 distributes electric power, suppliedfrom the battery 4, to the ECUs 1, 1, . . . , the actuators 2, 2, . . .and the noise detection apparatus 6, which are arranged in a vehicle.

One of the plurality of power lines 3, 3, . . . , branched from thejunction box 5, is connected to the associated one of the ECUs 1, 1, . .. . Thus, the ECU 1 can receive supply of electric power from thebattery 4. The power line 3 is also connected to the other one of theECUs 1, 1, . . . , and supplies electric power to this ECU 1. The powerline 3 through which the ECUs 1, 1 are connected to each other isbranched and connected to the actuator 2 via a switch. When the switchis ON, electric power from the battery 4 is supplied to the actuator 2,thereby operating the actuator 2.

It should be noted that each of the ECUs 1, 1, . . . and the actuators2, 2, . . . is internally configured so that the connected power line 3is connected via each constituent element and load included therein to abody ground.

Further, in the in-vehicle PLC system according to Embodiment 1, therespective ECUs 1, 1, . . . are not only capable of being operated inresponse to supply of electric power from the battery 4 via theassociated power lines 3, 3, . . . , but also capable of receiving andtransmitting data by superimposing communication carrier waves on thepower lines 3, 3, . . . through which the ECUs 1, 1, . . . are connectedto each other. Thus, in the in-vehicle PLC system, no communicationsignal line for reception/transmission of data used for running control,video data or the like has to be additionally provided between the ECUs1, 1, . . . . As a result, a reduction in the number of wires and areduction in weight can be achieved for a harness provided in thevehicle.

FIG. 7 is a block diagram illustrating internal configurations of theECU 1 and the noise detection apparatus 6, which are included in thein-vehicle PLC system according to Embodiment 1. The ECU 1 includes: acontrol section 10; a power supply circuit 11; a communication controlsection 12; a power line communication section 13; and a communicationsignal separation/coupling section 14.

Using a microcomputer, the control section 10 receives supply ofelectric power via the power supply circuit 11, and controls receptionand transmission of data performed by the communication control section12, or operations of other unillustrated constituent elements. The powersupply circuit 11 is connected to the control section 10, thecommunication control section 12, the power line communication section13 and other unillustrated constituent elements, and supplies electricpower to each of these constituent elements. For instance, the powersupply circuit 11 appropriately adjusts a driving voltage of 12 V, forexample, which is received from the battery 4 via the power line 3, to avoltage necessary for each of the constituent elements, and thensupplies the resulting voltage thereto.

Using a network controller, the communication control section 12realizes reception and transmission of various pieces of data, includingcontrol data, from and to the other ECUs 1, 1, . . . and actuators 2, 2,. . . . The data reception and transmission from and to the otherdevices by the communication control section 12 of the ECU 1 accordingto Embodiment 1 are performed in conformance with a FlexRay (registeredtrademark) protocol. It should be noted that the communication protocolis not limited to FlexRay, but may be CAN (Controller Area Network), LIN(Local Interconnect Network), etc.

The power line communication section 13 is a circuit for implementingthe functions of modulating a carrier wave by a signal at the time oftransmission, and decoding a signal from a carrier wave at the time ofreception. The communication signal separation/coupling section 14 is acircuit for implementing the functions of coupling a carrier wave to thepower line 3 at the time of transmission, and separating a carrier wavefrom the power line 3 at the time of reception. Addition of a power linecommunication function is enabled by removing an existing communicationsection from the existing communication control section 12 that performscommunication in conformance with FlexRay, and by adding the power linecommunication section 13 and the communication signalseparation/coupling section 14 instead of the existing communicationsection.

The noise detection apparatus 6 includes: a control section 60; astorage section 61; a temporary storage section 63; and a measurementsection 64. Using a CPU (Central Processing Unit), the control section60 executes noise detection processing based on a noise detectionprogram 62 stored in the storage section 61. Using a nonvolatile memorysuch as a hard disk, an EEPROM (Electrically Erasable and ProgrammableRead Only Memory) or a flash memory, the storage section 61 stores thenoise detection program 62, and further stores data of a detected noise.Using a memory such as a DRAM (Dynamic Random Access Memory) or an SRAM(Static Random Access Memory), the temporary storage section 63temporarily stores data generated by processing carried out by thecontrol section 60.

The measurement section 64 measures voltage values in the power line 3at a predetermined interval (that is sampling interval), and stores themeasurement results in the storage section 61 or the temporary storagesection 63. The measurement section 64 may have a plurality of terminalsso as to be able to measure voltage values at a plurality of measurementpoints in the power lines 3. The predetermined interval in themeasurement is 0.01 μsec (means the sampling frequency is 100 MHz), forexample.

It should be noted that for the noise detection apparatus 6, a personalcomputer may be used, or an FPGA, a DSP, an ASIC, etc., includingcomponents for performing functions of the respective constituentelements of the apparatus, may be used with the aim of providing theapparatus exclusively for noise detection.

Based on the noise detection program 62, the control section 60 of thenoise detection apparatus 6 performs functions illustrated in FIG. 8,and executes processing for detecting an impulsive noise from voltagevalues (observed noise sequence) measured and obtained for eachpredetermined interval by the measurement section 64. FIG. 8 is afunctional block diagram illustrating functions implemented by the noisedetection apparatus 6 included in the in-vehicle PLC system according toEmbodiment 1.

Based on the noise detection program 62, the control section 60functions as a parameter estimation section 601 and an impulsive noisedetection section 605. Functions of the parameter estimation section 601include; a function of an initial value deciding section 602 fordeciding an initial value of a parameter; a function of a BW algorithmcalculation section 603 for calculating, using a BW algorithm, a noisecharacteristic for maximization of the likelihood of the observed noisesequence; and a function of a parameter output section 604.

From the observed noise sequence obtained by the measurement section 64,the control section 60 extracts voltage value data for a predeterminedperiod (measurement periodical unit). For the extracted voltage valuedata, the control section 60 estimates and outputs a parameterindicative of a noise characteristic, and estimates and outputs a statesequence from the parameter by the functions of the parameter estimationsection 601. Using the estimated state sequence, the control section 60determines whether or not an impulsive noise is generated for eachsection (in units of the predetermined interval=0.01 μsec, which mayinclude one or a plurality of the predetermined intervals) in the periodby the function of the impulsive noise detection section 605.

Using the functions of the parameter estimation section 601, the controlsection 60 obtains parameters θ=(Q, N) including four state transitionprobabilities q₀₀, q₀₁, q₁₁ and q₁₀ (=Q) and N=[σ₀ ², σ₁ ²]^(T), andfurther obtains, from the parameters θ, the following parameters:

Channel Memory γ: γ=1/(q₀₁+q₁₀)

Impulsive noise occurrence probability P₁: P₁=q₀₁/q₀₁+q₁₀

Impulse-To-Background Noise Ratio R:R=σ_(I) ²/σ_(G) ²=(σ₁ ²−σ₀ ²)/σ₀ ²

Background Noise Power σ_(G) ²: σ_(G) ²=σ₀ ²

In order to obtain the above parameters, the control section 60 firstobtains initial values of the parameters θ, i.e., initial values of astate transition probability matrix Q (=q_(ss′), s, s′=0, 1) and averagenoise power N (=[σ₀ ², σ₁ ²]^(T)), by the function of the initial valuedeciding section 602. In this case, the control section 60 decides theinitial values based on the foregoing formulas 14 to 16. The controlsection 60 calculates the parameters θ (state transition probabilitiesand state noise power) for maximization of the likelihood of theobserved noise sequence by the function of the BW algorithm calculationsection 603. The calculation of the parameters θ for maximization of thelikelihood of the observed noise sequence is performed based on theforegoing formulas 10 to 12. Based on estimated values of the parametersθ for maximization of the likelihood of the observed noise sequence,calculated by the function of the BW algorithm calculation section 603,the control section 60 obtains the foregoing parameters (i.e., thechannel memory γ, impulsive noise occurrence probability P₁,impulse-to-background noise ratio R, and background noise power. σ_(G)²) and stores these parameters in the storage section 61 by theparameter output section 604. Further, the control section 60 obtains anestimated state sequence and outputs the estimated state sequence to theimpulsive noise detection section 605 by the parameter output section604.

The process of impulsive noise detection processing performed by thecontrol section 60 will be described in detail with reference to a flowchart. FIG. 9 is the flow chart illustrating an example of processingexecuted by the noise detection apparatus 6 according to Embodiment 1.

The control section 60 obtains measurement data (observed noisesequence) by the measurement section 64 (Step S1). Then, the controlsection 60 extracts data for a predetermined period, included in theobtained measurement data, and gives the extracted data to the parameterestimation section 601 (Step S2). In this case, the period is providedin units of communication cycles of FlexRay by way of example, and is 1msec in Embodiment 1. As mentioned above, the interval of measurement ofvoltage values by the measurement section 64 is 0.01 μsec, andtherefore, the extracted data is a sequence of voltage values for 100000samples (K=100000).

Based on the extracted data extracted for 1 msec (K=100000 samples ofvoltage values on the time series), the control section 60 calculatesthe parameters θ (noise characteristics) for maximization of thelikelihood of the observed noise sequence with the use of a hiddenMarkovian-Gaussian noise model by the functions of the initial valuedeciding section 602 and BW algorithm calculation section 603 of theparameter estimation section 601 (Step S3). The calculation of therespective parameters indicative of the noise characteristics will bedescribed later in detail with reference to a flow chart of FIG. 10.

Based on the parameters θ calculated in Step S3, the control section 60estimates and calculates an estimated state sequence by the function ofthe parameter output section 604 of the parameter estimation section 601(Step S4), and further obtains the parameters (i.e., the channel memoryγ, impulsive noise occurrence probability P₁, impulse-to-backgroundnoise ratio R, and background noise power σ_(G) ²) indicative of thenoise characteristics (Step S5).

Based on the noise characteristics calculated in Step S5, the controlsection 60 determines, for each period of 1 msec, whether or not animpulsive noise is generated in this period by the function of theimpulsive noise detection section 605 (Step S6). It should be noted thatdeterminations are made based on the parameters (γ, P₁, R, and σ_(G) ²)as follows. For example, the determination is made based on whether ornot the channel memory γ is higher than a predetermined value (e.g.,10). Furthermore, when the impulsive noise occurrence probability P₁ isequal to or higher than 0.5, the control section 60 can determine by thefunction of the impulsive noise detection section 605 that the obtainedobserved noise sequence has a white Gaussian noise but has no impulsivenoise.

When it is determined that no impulsive noise is generated (S6: NO), thecontrol section 60 ends the detection processing without any furtherstep. It should be noted that Steps S5 and S6 are not absolutelynecessary. In other words, the parameters (i.e., the channel memory γ,impulsive noise occurrence probability P₁, impulsive-to-background noiseratio R, and background noise power. σ_(G) ²) do not necessarily have tobe calculated, and the presence or absence of generation of an impulsivenoise does not necessarily have to be determined for each period.

When it is determined by the function of the impulsive noise detectionsection 605 that an impulsive noise is generated (S6: YES), the controlsection 60 detects, based on the estimated state sequence, the impulsivenoise at each predetermined interval during the period included in theextracted data (Step S7), and stores noise data including the detectedresult in the storage section 61 (Step S8), thus ending the processing.In Step S7, an impulsive noise is detected for each predeterminedinterval (which is 0.01 μsec in Embodiment 1). However, the presentinvention is not limited to this, but an impulsive noise may be detectedon a bit-by-bit basis by handling two or more samples as a single bit.

The noise data stored in Step S8 may be the noise characteristics (θ, orγ, P₁, R and σ_(G) ²) calculated in Step S3 or Step S5, or may includethe extracted data (observed noise sequence). Moreover, the noise datamay include the state sequence estimated in Step S4. This is useful, forexample, in reproducing an impulsive noise using the noise data.

FIG. 10 is a flow chart illustrating an example of a procedure ofprocessing for calculating the parameters θ (noise characteristics) formaximization of the likelihood of an observed noise sequence based on aBW algorithm using a moment method, executed by the functions of theparameter estimation section 601 of the noise detection apparatus 6according to Embodiment 1. The processing procedure illustrated in FIG.10 is associated with the details of Step S3 in the flow chart of FIG.9.

From the given extracted data, i.e., from the voltage values forK=100000 samples, the control section 60 calculates three moments a, band c by the function of the initial value deciding section 602 usingthe foregoing formula 14 based on the moment method (Step S301)

Then, the control section 60 obtains an initial value of the noise powerN (=[σ₀ ², σ₁ ²]^(T), an estimated value symbol of which is abbreviated)by the function of the initial value deciding section 602. Therefore,the control section 60 calculates standard deviations σ₀ and σ₁ ofdistribution of noises in the respective states by the formulas 15 and16 based on the three moments a, b and c calculated in Step S301 (StepS302). From the standard deviations σ₀ and σ₁ of distribution of noisesin the respective states, the control section 60 calculates an initialestimated value of the noise power N of the extracted data by thefunction of the initial value deciding section 602 (Step S303).

Next, the control section 60 obtains an initial value of the statetransition probability matrix Q by the function of the initial valuedeciding section 602. Therefore, from the estimated values of thestandard deviations σ₀ and σ₁ of distribution of noises in therespective states calculated by the formulas 15 and 16 in Step S302, thecontrol section 60 calculates a threshold A for each voltage value of100000 samples of the extracted data (observed noise sequence) by usingthe formula 18 (Step S304). Then, using the formula 18, the controlsection 60 calculates an estimated state matrix s(k=1 to K) by making acomparison between: each voltage value of 100000 samples of theextracted data; and the calculated threshold Λ (Step S305). Furthermore,from the calculated estimated state matrix s(k=1 to K), the controlsection 60 calculates an initial estimated value of the matrix Q of thefour state transition probabilities (Step S306). Specifically, from theestimated state matrix s(k=1 to K), the control section 60 obtains eachof the four numbers A_(ss′) of state transitions from the state s (=0or 1) to the state s′ (=0 or 1), and obtains the number A_(s) of eachstate s (=0 or 1), thus obtaining the state transition probabilityinitial value q_(ss′)(q_(ss′)=A_(ss′)/A_(s)).

Using the function of the initial value deciding section 602, thecontrol section 60 decides, as the initial estimated values of theparameters θ, the initial estimated values of Q and N calculated inSteps S303 and S306 (Step S307).

Next, utilizing the initial values decided in Step S307, the controlsection 60 calculates the parameters θ (noise characteristics) formaximization of the likelihood of the observed noise sequence by thefunction of the BW algorithm calculation section 603 with the use of theBW algorithm and MAP estimation. In other words, the parameters θ to becalculated are values for increasing the likelihood of the obtainedobserved noise sequence for the given initial values. More specifically,the control section 60 first assigns 0 into the number of calculations 1(Step S308), adds 1 thereto (Step S309), and calculates a forward stateprobability (Forward probability) α_(k)(s) and a backward stateprobability (Backward probability) β_(k)(s) based on the foregoingformulas 1 to 6 (Step S310).

Using the forward state probability α_(k)(s) and backward stateprobability β_(k)(s) calculated in Step S310, the control section 60calculates the parameters θ (estimated values of the state transitionprobability matrix Q and noise power N) by the foregoing formulas 10 and11 (Step S311).

For the estimated values of the parameters θ derived in Step S311, thecontrol section 60 determines, using the function of the BW algorithmcalculation section 603, whether or not a logarithmic likelihood ishigher than a threshold Δ, or whether or not the number of calculations1 is equal to or higher than an upper limit value L (Step S312). Whenthe logarithmic likelihood is equal to or lower than the threshold Δ andthe number of calculations 1 is below the upper limit value L (S312:NO), the control section 60 returns the processing to Step S309 andrepeats the processing in order to obtain a higher likelihood value.

When it is determined by the function of the BW algorithm calculationsection 603 that for the estimated values of the parameters θ derived inStep S311, the logarithmic likelihood is higher than the threshold Δ orthe number of calculations 1 is equal to or higher than the upper limitvalue L (S312: YES), the control section 60 ends the processing forobtaining the parameters θ for maximization of the likelihood of theobserved noise sequence, and returns the processing to Step S4 in theflow chart of FIG. 9.

As described above, the parameters θ (=(Q, N)) of the hiddenMarkovian-Gaussian noise model, calculated by the parameter estimationsection 601 of the control section 60, are calculated in such a mannerthat subjective initial values, thresholds or the like given by human asdetection conditions are removed to the extent possible. Using theestimated state sequence estimated in Step S4 of the flow chart of FIG.9 (by the formulas 5 to 8), an impulsive noise can be detected with highaccuracy.

The accuracy of detection of an impulsive noise by the noise detectionapparatus 6 according to Embodiment 1 was evaluated by obtaining adetection error probability. The detection error probability wasmeasured by using a known two-state hidden Markovian-Gaussian noise.First, the channel memory γ, impulsive noise occurrence probability P₁and impulsive-to-background noise ratio R were given, and a statesequence s(k=1 to K (=20000)) and a noise sequence were generated. Usingthe generated state sequence s as a true state sequence, impulsive noisedetection was performed on the generated noise sequence by the noisedetection apparatus 6 according to Embodiment 1. Specifically, theimpulsive noise detection was performed by the following method. Forpurposes of comparison, the threshold Λ calculated by the foregoingformulas 14 to 18 was used, and the detection was performed based onlyon whether or not each voltage value of the noise sequence was higherthan the threshold Λ, thus detecting an impulsive noise when the valueis higher than the threshold Λ (this method will be referred to as a“Moment-ML method”). In the Moment-ML method, Steps S307 to S312 are notperformed.

FIGS. 11 and 13 are graphs each illustrating a detection errorprobability caused by the noise detection apparatus 6 according toEmbodiment 1.

In FIG. 11, the horizontal axis represents a change in the channelmemory γ, and the vertical axis represents a detection error probabilitythereof. Fixed values are given to the impulsive noise occurrenceprobability P₁ and the impulse-to-background noise ratio R so thatP₁=0.01, and R=100. FIG. 11 illustrates the error probability of thechannel memory γ calculated for noise sequences generated with thechannel memory γ changed from 1 to 100. The 2 lines in FIG. 11 indicatethe detection error probability caused by the Moment-ML method, and thedetection error probability caused by the noise detection apparatus 6according to Embodiment 1 (which is represented by “BW-MAP” in FIGS. 11to 13), respectively.

In FIG. 12, the horizontal axis represents a change in the impulsivenoise occurrence probability P₁, and the vertical axis represents adetection error probability thereof. Fixed values are given to thechannel memory γ and the impulse-to-background noise ratio R so thatγ=10, and R=100. FIG. 12 illustrates the error probability of theimpulsive noise occurrence probability P₁ calculated for noise sequencesgenerated with the impulsive noise occurrence probability P₁ changedfrom 0.001 to 0.01. The 2 lines in FIG. 12 indicate the detection errorprobability caused by the Moment-ML method, and the detection errorprobability caused by the noise detection apparatus 6 according toEmbodiment 1, respectively.

In FIG. 13, the horizontal axis represents a change in theimpulsive-to-background noise ratio R, and the vertical axis representsa detection error probability thereof. Fixed values are given to thechannel memory γ and the impulsive noise occurrence probability P₁ sothat γ=10, and P₁=0.01. FIG. 13 illustrates the error probability of theimpulsive-to-background noise ratio R calculated for noise sequencesgenerated with the impulsive-to-background noise ratio R changed from 10to 1000. The 2 lines in FIG. 13 indicate the detection error probabilitycaused by the Moment-ML method, and the detection error probabilitycaused by the noise detection apparatus 6 according to Embodiment 1,respectively.

As illustrated in the graphs of FIGS. 11 to 13, the impulsive noisedetection method, performed by the noise detection apparatus 6 accordingto Embodiment 1 using the BW algorithm and MAP estimation, exhibitsclear superiority. It should be noted that as illustrated in FIG. 13,the detection error probability is monotonously decreased with respectto an increase in the impulse-to-background noise ratio R. This meansthat the noise power σ_(I) ² of an impulsive noise is increased withrespect to the background noise power σ_(G) ², thus increasing theaccuracy of distinction between a period during which an impulsive noiseis generated and a period during which no impulsive noise is generated.

On the other hand, as illustrated in FIGS. 11 and 12, in the impulsivenoise detection method performed by the noise detection apparatus 6according to Embodiment 1, minimum values of the detection errorprobabilities exist for the changes in the channel memory γ and theimpulsive noise occurrence probability P₁, and therefore, it can be seenthat there exist the channel memory γ and impulsive noise occurrenceprobability P₁, which enable accurate detection of generation of animpulsive noise. In this case, since the accuracy of estimation for thechannel memory γ and impulsive noise occurrence probability P₁ dependson an extracted data length (K) in the observed noise sequence, analysishas to be conducted in consideration of K.

FIG. 14 is a waveform diagram illustrating impulsive noise detectionresults obtained by the noise detection apparatus 6 according toEmbodiment 1. FIG. 14 corresponds to a waveform diagram of noises in thein-vehicle PLC system illustrated in FIGS. 38 to 40. Sections indicatedby arrows in FIG. 14 are detected as sections in which generation ofimpulsive noises is detected by the noise detection apparatus 6. Theaccuracy of detection of a “state in which an impulsive noise ingenerated” is increased as compared with a case where a conventionalmethod of only making a comparison between an amplitude value and athreshold, for example, is performed.

When a comparison is made between the method for detecting, fromobserved voltage values, an impulsive noise based on a threshold by theMoment-ML method, and the detection method performed by the noisedetection apparatus 6 according to Embodiment 1, it can be seen thataccurate impulsive noise estimation and detection are enabled by thenoise detection apparatus 6 as illustrated in FIG. 15. FIG. 15 is anexplanatory diagram illustrating examples of noise characteristics of animpulsive noise detected by the noise detection apparatus 6 according toEmbodiment 1. For purposes of comparison, exemplary details of therespective parameters estimated by the foregoing Moment-ML method arealso illustrated in FIG. 15.

The noise characteristic parameters estimated and calculated for animpulsive noise detected by the noise detection apparatus 6 according toEmbodiment 1 are preferably stored in the storage section 61. Thesepieces of information are useful as impulsive noise information obtainedautomatically from statistical properties of the observed noisesequence.

Embodiment 2

In Embodiment 2, a Determination is Further Made Using statisticalinformation in the process of impulsive noise detection processingperformed by the noise detection apparatus 6 described in Embodiment 1.In other words, measurement data including only a Gaussian noise is notsubjected to an estimation process that uses a BW algorithm and MAPestimation. Thus; the detection accuracy can be increased.

Configurations of an in-vehicle PLC system and a noise detectionapparatus 6 according to Embodiment 2 are similar to those of thein-vehicle PLC system and noise detection apparatus 6 according toEmbodiment 1, and Embodiment 2 differs from Embodiment 1 only in detailsof the processing executed by the noise detection apparatus 6.Accordingly, in the following description, constituent elements commonto those of Embodiment 1 are identified by the same referencecharacters, and detailed description thereof will be omitted.

FIG. 16 is a functional block diagram illustrating functions implementedby the noise detection apparatus 6 according to Embodiment 2. Based onthe noise detection program 62, the control section 60 functions as theparameter estimation section 601 and the impulsive noise detectionsection 605, and further functions as an information criteriondetermining section 606. Using statistical information that is based onparameters calculated from voltage value data extracted from an observednoise sequence, the control section 60 determines the presence orabsence of an impulsive noise by the function of the informationcriterion determining section 606 prior to a process performed based ona BW algorithm. When it is determined by the function of the informationcriterion determining section 606 that no impulsive noise exists, thecontrol section 60 skips the process performed by the BW algorithmcalculation section 603. In this case, the control section 60 obtains anestimated state sequence and noise power based on the assumption thatonly a white Gaussian noise is included and outputs the estimated statesequence and noise power to the impulsive noise detection section 605 bythe function of the parameter output section 604.

FIG. 17 is a flow chart illustrating an example of processing executedby the noise detection apparatus 6 according to Embodiment 2. It shouldbe noted that, of the following processing steps, processing stepscommon to those illustrated in the flow chart of FIG. 9 according toEmbodiment 1 are identified by the same step numbers, and detaileddescription thereof will be omitted.

Based on extracted data for 1 msec extracted in Step S2, the controlsection 60 obtains an initial estimated value of a noise power matrix N(=[σ₀ ², σ₁ ²]^(T)) and an initial estimated value of a steady-stateprobability matrix P (=[P₀P₁]^(T)) of each state by the initial valuedeciding section 602 of the parameter estimation section 601 using amoment method (Step S51). It should be noted that in Embodiment 2, asampling frequency is set at 200 MHz, and therefore, the extracted datais voltage values obtained on the time series for K=200000 samples.

From the initial values calculated in Step S51, the control section 60calculates, using the function of the information criterion determiningsection 606, information concerning a criterion for macroscopicallydetermining whether or not an impulsive noise is included in theextracted data (Step S52). Details of Step S52 will be described later.

Based on the information calculated in Step S52, the control section 60determines whether or not an impulsive noise is included in theextracted data by the function of the information criterion determiningsection 606 (Step S53). Specifically, it is determined in Step S53whether or not an after-mentioned fifth criterion is satisfied, i.e.,whether or not the initial estimated value P₁ of the steady-stateprobability of the foregoing impulse-generated state falls within therange of 0≦P₁<0.5 (first criterion) and whether or not anafter-mentioned formula 32 (fifth criterion) is satisfied.

When it is determined in Step S53 that no impulsive noise is included inthe extracted data (S53: NO), the control section 60 estimates andcalculates an estimated state sequence and calculates a noisedistribution o based on the assumption that a noise included in theextracted data is a white Gaussian noise (Step S54), thus ending theprocessing. It should be noted that the control section 60 may store, inthe storage section 61, the estimated state sequence and noisedistribution a estimated and calculated based on the assumption that thenoise is a white Gaussian noise.

On the other hand, when it is determined in Step S53 that an impulsivenoise is included in the extracted data (S53: YES), the control section60 obtains an initial estimated value of a matrix Q of four statetransition probabilities based on an ML (maximum likelihood) method fromthe initial estimated value of the noise power N calculated in Step S51(Step S55).

Next, from the initial values calculated in Steps S51 and S55 by thefunction of the initial value deciding section 602, the control section60 decides the initial estimated values of Q and N as initial estimatedvalues of the parameters θ (Step S56).

Using the initial values decided in Step S56, the control section 60calculates the parameters θ (noise characteristics) for maximization ofthe likelihood of the observed noise sequence based on the BW algorithm(Step S57). Next, based on the parameters θ=(Q, N) calculated in StepS57, the control section 60 estimates and calculates an estimated statesequence (Step S58).

Then, based on the estimated and calculated state sequence, the controlsection 60 detects an impulsive noise at each time point (Step S7), andstores the impulsive noises in the storage section 61 (Step S8), thusending the processing.

It should be noted that the calculation of the information in Step S52and the step of determining whether or not an impulsive noise isincluded in the extracted data in Step S53 may be carried out in adifferent order. For example, when not only the after-mentioned criteriabut also the initial value of the matrix Q are used, the calculationstep in Step S52 is carried out after Step S55 or Step S5.6.

Regarding Step S52, examples of criteria for determining whether or notan impulsive noise is included in the extracted data include the firstto fifth criteria. It should be noted that in Embodiment 2, both of thefirst and fifth criteria are adopted as mentioned above. In thisembodiment, the first to fifth criteria are as follows.

First Criterion: Rarity of Impulsive Noise

0≦P₁<0.5

The first criterion is provided based on the assumption that thesteady-state probability is less than ½ from the rarity of an impulsivenoise because an impulsive noise is not frequently generated but isaccidentally generated.

Second Criterion: Logarithmic Likelihood

For the second criterion, in addition to the first criterion,logarithmic likelihoods of amplitude probability distributions ofobserved noise sequences are used. A logarithmic likelihood is givenwhen the observed noise sequence, i.e., the extracted data, is asequence including only a white Gaussian noise and the amplitudeprobability distribution is a Gaussian distribution. Another logarithmiclikelihood is given when the extracted data is a sequence also includingan impulsive noise and the amplitude probability distribution is amixture Gaussian distribution. A comparison is made between the formerlogarithmic likelihood and the latter logarithmic likelihood todetermine which logarithmic likelihood is higher. Then, when the latterlogarithmic likelihood, i.e., the logarithmic likelihood of theamplitude probability distribution that is a mixture Gaussiandistribution, is higher than the former logarithmic likelihood, i.e.,the logarithmic likelihood of the amplitude probability distributionthat is a Gaussian distribution, there is provided the criterion fordetermining that the extracted data includes an impulsive noise.

In this case, the amplitude probability distribution of the observednoise sequence n(k=1 to K) can be expressed by the following formula 21,and the logarithmic likelihood thereof is defined by the followingformula 22.

[Exp. 16]

p(n_(k)|{circumflex over (θ)})  (21)

l({circumflex over (θ)})=E _(k)[ln p(n _(k)|{circumflex over(θ)})]  (22)

The logarithmic likelihood of a mixture Gaussian distribution and thatof a Gaussian distribution are each expressed by the following formula23. It should be noted that the logarithmic likelihood of a Gaussiandistribution is represented as in the following formula 24.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 17} \right\rbrack & \; \\{{{l_{GM}\left( {\hat{\theta}}_{GM} \right)} = {E_{K}\left\lbrack {\ln \; {p_{GM}\left( {n_{k}{\hat{\theta}}_{GM}} \right)}} \right\rbrack}}{{l_{G}\left( {\hat{\sigma}}^{2} \right)} = {E_{K}\left\lbrack {\ln \; {p_{G}\left( {n_{k}{\hat{\sigma}}^{2}} \right)}} \right\rbrack}}} & (23) \\{{l_{G}\left( {\hat{\sigma}}^{2} \right)} = {{- \frac{1}{2}}\left( {1 + {\ln \; \left( {2\pi {\hat{\sigma}}^{2}} \right)}} \right)}} & (24)\end{matrix}$

When the latter logarithmic likelihood, i.e., the logarithmic likelihoodof the amplitude probability distribution that is a mixture Gaussiandistribution, is higher than the former logarithmic likelihood, i.e.,the logarithmic likelihood of the amplitude probability distributionthat is a Gaussian distribution, it is determined that the extracteddata includes an impulsive noise. Therefore, satisfaction of thefollowing formula 25 by the mixture Gaussian distribution can be definedas the second criterion.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 18} \right\rbrack & \; \\{{{l_{GM}\left( {\hat{\theta}}_{GM} \right)} > {l_{G}\left( {\hat{\sigma}}^{2} \right)}}\therefore{{l_{GM}\left( {\hat{\theta}}_{GM} \right)} > {{- \frac{1}{2}}\left( {1 + {\ln \; \left( {2\pi {\hat{\sigma}}^{2}} \right)}} \right)}}} & (25)\end{matrix}$

Third Criterion Takeuchi Information Criterion (TIC)

For the third criterion, in addition to the first criterion, TIC, wellknown as an index for evaluating the likelihood of a model, is used. Theinitial estimated values of the parameters θ, estimated and calculatedusing an ML (maximum likelihood) method (i.e., Moment-ML method) thatutilizes a moment method, are not estimated values that are based on atrue distribution. TIC is known as an information criterion to which acorrection term for a deviation from the true distribution is added.

The correction term of TIC when the parameters θ estimated by the MLmethod and the amplitude probability distribution of the observed noisesequence n(k=1 to K) are given is defined by the following formula 26.It should be noted that Tr in the formula 26 represents the trace of amatrix, and I(θ) and J(θ) are p×p Fisher information matrices defined bythe following formulas 27 and 28, respectively. It should be noted thatp in the p×p Fisher information matrices represents the number of freeparameters included in the parameters θ of the model.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 19} \right\rbrack & \; \\{{c\left( \hat{\theta} \right)} = {{Tr}\left( {{I\left( \hat{\theta} \right)}{J^{- 1}\left( \hat{\theta} \right)}} \right)}} & (26) \\{{I\left( \hat{\theta} \right)} = {{E_{K}\left\lbrack {\frac{{\partial\ln}\; {p\left( {n_{k}\theta} \right)}}{\partial\theta}\frac{{\partial\ln}\; {p\left( {n_{k}\theta} \right)}}{\partial\theta^{T}}} \right\rbrack}_{\theta = \hat{\theta}}}} & (27) \\{{J\left( \hat{\theta} \right)} = {{- {E_{K}\left\lbrack \frac{{{\partial^{2}\ln}\; {p\left( {n_{k}\theta} \right)}}\;}{{\partial\theta}{\partial\theta^{T}}} \right\rbrack}}_{\theta = \hat{\theta}}}} & (28)\end{matrix}$

In the third criterion that uses TIC, the value of TIC, is given whenthe extracted data is a sequence including only a white Gaussian noiseand the amplitude probability distribution is a Gaussian distribution,the value of TIC_(gm) is given when the extracted data includes animpulsive noise and the amplitude probability distribution is a mixtureGaussian distribution, and a comparison is made between the values ofTIC_(g) and TIC_(gm) to determine which value is higher. Then, whenTIC_(g)>TIC_(gm), there is provided the criterion for determining thatno impulsive noise is included in the extracted data, but whenTIC_(g)<TIC_(gm), there is provided the criterion for determining thatan impulsive noise is included in the extracted data.

Accordingly, to be more specific, satisfaction of the following formula29 by the logarithmic likelihood of a mixture Gaussian distribution andthe value resulting from addition of the correction term thereof can bedefined as the third criterion.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 20} \right\rbrack & \; \\{{{l_{GM}\left( {\hat{\theta}}_{GM} \right)} - \frac{c_{GM}\left( {\hat{\theta}}_{GM} \right)}{K}} > {{- \frac{1}{2}}\left( {\frac{K - 1}{K} + {\ln \left( {2\pi {\hat{\sigma}}^{2}} \right)} + \frac{E_{K}\left\lbrack n_{k}^{4} \right\rbrack}{n^{2}{\hat{\sigma}}^{4}}} \right)}} & (29)\end{matrix}$

c_(GM)({circumflex over (θ)}_(GM)): Correction Term of TIC for MixtureGaussian Distribution

Fourth Criterion Akaike Information Criterion (AIC)

For the fourth criterion, in addition to the first criterion, AIC isused. For the correction term of TIC of the third criterion, a processfor a sample mean E_(k), which is based on an empirical distribution, isperformed on the Fisher information matrices. Therefore, instability iscaused due to numerical calculation of the sample mean process. AnAkaike information criterion (AIC), which removes such instabilityresulting from numerical calculation, is known. When modeled probabilitydensity functions p(n_(k)|θ) include a true probability densityfunction, the Fisher information matrices satisfy I(θ₀)=J(θ₀), where θ₀represents an ML estimated value from the true distribution. Hence, inAIC, a correction term c(θ) for the amplitude probability distributionof the observed noise sequence n(k=1 to K) is set as p (formula 30).

[Exp. 21]

c({circumflex over (θ)})=p  (30)

Thus, to be more specific, satisfaction of the following formula 31 canbe defined as the fourth criterion in AIC.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 22} \right\rbrack & \; \\{{{l_{GM}\left( {\hat{\theta}}_{GM} \right)} - \frac{3}{K}} > {{- \frac{1}{2}}\left( {\frac{K + 2}{K} + {\ln \left( {2\pi {\hat{\sigma}}^{2}} \right)}} \right)}} & (31)\end{matrix}$

Fifth Criterion: Criterion Defined by Number of Free Parameters

For the fifth criterion, in addition to the first criterion, attentionis given to the number of free parameters which serves as a correctionterm of AIC, and a criterion is defined by utilizing the number of freeparameters. When a mixture Gaussian distribution includes a truedistribution, a correction term of AIC is derived as follows:c_(GM)(θ_(GM))=3. On the other hand, when a Gaussian distributionincludes a true distribution, a correction term of AIC is derived asfollows: c_(G)(σ²)=1. Therefore, it is expected that a value close to 1is derived as the correction term of AIC when the amplitude probabilitydistribution of the observed noise sequence is a Gaussian distribution,and a value greater than 3 is derived as the correction term of AIC whenthe amplitude probability distribution of the observed noise sequence isa mixture Gaussian distribution. Thus, satisfaction of the followingformula 32 by utilizing the value of the correction term is defined asthe fifth criterion for determining that the extracted data includes animpulsive noise.

When the amplitude probability distribution of the observed noisesequence is a mixture Gaussian distribution, that is the observed noisesequence includes impulsive noise, a value greater than 3 is derived asthe correction term of AIC. Therefore right-hand value of formula 32 isassumed to be a value 2, that is z value is 1. However, the right-handvalue of formula 32 should not to be fixed to value 2, so that z valueis fine-tuned from 1.

$\begin{matrix}\left\lbrack {{Exp}.\mspace{14mu} 23} \right\rbrack & \; \\{{\frac{E_{K}\left\lfloor n_{k}^{4} \right\rfloor}{2\; K{\hat{\sigma}}^{4}} - \frac{1}{2}} > {1 + z}} & (32)\end{matrix}$

In Step S53, using the criteria calculated as described above, it isdetermined in advance whether or not the observed noise sequence is oneincluding an impulsive noise. Next, details of processing procedureillustrated in the flow chart of FIG. 17 will be described.

FIG. 18 is a flow chart illustrating details of processing forcalculating initial values of noise power and steady-state probabilityusing a moment method by the noise detection apparatus 6 according toEmbodiment 2. FIG. 18 corresponds to the details of Step S51 in theprocessing procedure illustrated in the flow chart of FIG. 17.

From the given extracted data, i.e., from the voltage values forK=200000 samples, the control section 60 calculates three moments a, band c by the function of the initial value deciding section 602 usingthe foregoing formula 14 based on the moment method (Step S61).

Then, the control section 60 obtains a noise power initial value N (=[σ₀², σ₁ ²]^(T), an estimated value symbol of which is abbreviated) by thefunction of the initial value deciding section 602. Therefore, thecontrol section 60 calculates standard deviations σ₀ and σ₁ ofdistribution of noises in the respective states by the formulas 15 and16 based on the three moments a, b and c calculated in Step S61 (StepS62). From the standard deviations σ₀ and σ₁ of distribution of noisesin the respective states, the control section 60 calculates an initialestimated value of the noise power N of the extracted data by thefunction of the initial value deciding section 602 (Step S63).

Furthermore, from the moments calculated in Step S61, the controlsection 60 calculates an initial value of the steady-state probabilityof each state based on the formula 17 by the function of the initialvalue deciding section 602 (Step S64), and returns the processing toStep S52 in the processing procedure illustrated in the flow chart ofFIG. 17.

FIG. 19 is a flow chart illustrating details of processing forcalculating four state transition probabilities by the noise detectionapparatus according to Embodiment 2. FIG. 19 corresponds to details ofStep S55 in the processing procedure illustrated in the flow chart ofFIG. 17.

The control section 60 obtains an initial value of the state transitionprobability matrix Q by the function of the initial value decidingsection 602. Therefore, from the estimated values of the standarddeviations σ₀ and σ₁ of distribution of noises in the respective statescalculated by the formulas 15 and 16 in Step S62 illustrated in the flowchart of FIG. 18, the control section 60 calculates a threshold Λ foreach voltage value of 200000 samples of the extracted data (observednoise sequence) by using the formula 18 (Step S71). Then, using theformula 18, the control section 60 calculates an estimated state matrixs(k=1 to K) by making a comparison between each voltage value of 200000samples of the extracted data; and the calculated threshold Λ (StepS72). Furthermore, from the calculated estimated state matrix s(k=1 toK), the control section 60 calculates an initial estimated value of thematrix Q of the four state transition probabilities (Step S73).Specifically, from the estimated state matrix s(k=1 to K), the controlsection 60 obtains each of the four numbers A_(ss′) of state transitionsfrom the state s (=0 or 1) to the state s′ (=0 or 1), and obtains thenumber A_(s) of each state s (=0 or 1), thus obtaining the statetransition probability initial value q_(ss′) (q_(ss′)=A_(ss′)/A_(s)).

Upon calculation of the estimated value of the matrix Q, the controlsection 60 returns the processing to Step S56 in the processingprocedure illustrated in the flow chart of FIG. 17.

FIG. 20 is a flow chart illustrating an example of a procedure ofprocessing for calculating parameters θ (noise characteristics) formaximization of the likelihood of an observed noise sequence based on aBW algorithm by the noise detection apparatus 6 according to Embodiment2. It should be noted that the processing procedure illustrated in theflow chart of FIG. 20 corresponds to details of Step S57 in theprocessing procedure illustrated in the flow chart of FIG. 17.

When the initial estimated values of the parameters θ decided in StepS56 are given, the control section 60 calculates, using the BW algorithmand MAP estimation, the parameters θ (noise characteristics) formaximization of the likelihood of the observed noise sequence by thefunction of the BW algorithm calculation section 603 (which aremaximization expected values for increasing the likelihood of theobtained observed noise sequence for the given initial values).

More specifically, the control section 60 first assigns 0 into thenumber of calculations 1 (Step S81), adds 1 thereto (Step S82), andcalculates a forward state probability (Forward probability) α_(k)(s)and a backward state probability (Backward probability) β_(k)(s) basedon the foregoing formulas 1 to 6 (Step S83). Using the forward stateprobability α_(k)(s) and the backward state probability β_(k)(s)calculated in Step S83, the control section 60 calculates the parametersθ (noise characteristics: estimated values of the state transitionprobability matrix Q and noise power N) by the foregoing formulas 10 and11 (Step S84).

For the estimated values of the parameters θ derived in Step S84, thecontrol section 60 determines, using the function of the BW algorithmcalculation section 603, whether or not a logarithmic likelihood ishigher than a threshold A, or whether or not the number of calculations1 is equal to or higher than an upper limit value L (Step S85). When thelogarithmic likelihood is equal to or lower than the threshold A and thenumber of calculations 1 is below the upper limit value L (S85: NO), thecontrol section 60 returns the processing to Step S82 and repeats theprocessing in order to obtain a higher likelihood value.

When it is determined by the function of the BW algorithm calculationsection 603 that for the estimated values of the parameters θ derived inStep S84, the logarithmic likelihood is higher than the threshold Δ orthe number of calculations 1 is equal to or higher than the upper limitvalue L (S85: YES), the control section 60 ends the processing forobtaining the parameters θ (noise characteristics) for maximization ofthe likelihood of the observed noise sequence, and returns theprocessing to Step S58 in the flow chart of FIG. 17.

Evaluations were conducted on the accuracy of impulsive noise detectionperformed by the noise detection apparatus 6 according to Embodiment 2.A detection error probability measurement method of Embodiment 2 issimilar to that of Embodiment 1. It is to be noted that as for thecriterion-related information and the criteria for determining whetheror not an impulsive noise is included, which are illustrated in StepsS52 and S53, the determination criteria were changed based on the firstto fifth criteria, and detection error probabilities associated with therespective criteria were calculated.

FIGS. 21 to 23 are graphs each illustrating detection errorprobabilities caused by the noise detection apparatus 6 according toEmbodiment 2.

In FIG. 21, the horizontal axis represents a change in the channelmemory γ, and the vertical axis represents detection error probabilitiesthereof. Fixed values are given to the impulsive noise occurrenceprobability P₁ and the impulse-to-background noise ratio R so thatP₁=0.001, and R=˜100. FIG. 21 illustrates the error probabilities of thechannel memory γ calculated for noise sequences generated with thechannel memory γ changed from 10 to 1000. The dotted line and hollowrhombuses in FIG. 21 indicate the detection error probability when thefirst criterion is used, and the chain double-dashed line and hollowinverted triangles in FIG. 21 indicate the detection error probabilitywhen the second criterion is used. Further, the dashed line andasterisks in FIG. 21 indicate the detection error probability when thethird criterion is used, the broken line and crosses in FIG. 21 indicatethe detection error probability when the fourth criterion is used, andthe solid line and X marks in FIG. 21 indicate the detection errorprobability when the fifth criterion is used.

In FIG. 22, the horizontal axis represents a change in the impulsivenoise occurrence probability P₁, and the vertical axis representsdetection error probabilities thereof. Fixed values are given to thechannel memory γ and the impulse-to-background noise ratio R so thatγ=100, and R=100. FIG. 22 illustrates the error probabilities of theimpulsive noise occurrence probability P₁ calculated for noise sequencesgenerated with the impulsive noise occurrence probability P₁ changedfrom 0.0001 to 0.01. Explanatory legends of FIG. 22 for the respectivecriteria are similar to those of FIG. 21.

In FIG. 23, the horizontal axis represents a change in theimpulse-to-background noise ratio R, and the vertical axis representsdetection error probabilities thereof. Fixed values are given to thechannel memory γ and the impulsive noise occurrence probability P₁ sothat γ=100, and P₁=0.001. FIG. 23 illustrates the error probabilities ofthe impulse-to-background noise ratio R calculated for noise sequencesgenerated with the impulse-to-background noise ratio R changed from 10to 1000. Explanatory legends of FIG. 23 for the respective criteria aresimilar to those of FIG. 21.

As compared with the detection error probabilities illustrated in FIGS.11 to 13 according to Embodiment 1, it can be seen that the detectionaccuracy is increased by performing the determination processes usingthe criteria in Steps S52 and S53 illustrated in the flow chart of FIG.17. It should be noted that even when the second to fourth criteria areused, the accuracy is increased as compared with the case where only thefirst criterion is used, and the accuracy is further increased by usingthe fifth criterion in particular. Especially, even when the statetransition probability q₀₁ from the Gaussian noise generated state “0”to the impulsive noise generated state “1” is extremely low, thedetection error probability can be decreased.

As described above, using the statistical information, the extracteddata including no impulsive noise is excluded from objects to which theBW algorithm and MAP estimation are applied, thus making it possible toprevent a white Gaussian noise from being forcefully detected as animpulsive noise, and to further increase the impulsive noise detectionaccuracy.

Embodiment 3

In Embodiment 3, Observed Noise Sequences are Obtained under knownsituations, results of calculation of state sequences and noisecharacteristics performed by the noise detection apparatus 6 accordingto Embodiment 1 or 2 are obtained, impulsive noise frequencies detectedusing the calculation results are calculated, the calculation resultsand impulsive noise frequencies are stored in association with therespective situations, and then a simulation is carried out using thenoise characteristics and impulsive noise frequencies.

FIG. 24 is a block diagram illustrating a configuration of a simulationapparatus according to Embodiment 3. Using a personal computer, thesimulation apparatus 7 includes: a control section 70; a storage section71; a temporary storage section 74; a condition input section 75; and apseudonoise generation section 76. Using a CPU, the control section 70executes a simulation based on a simulation program 72 stored in thestorage section 71. Using a nonvolatile memory such as a hard disk, anEEPROM or a flash memory, the storage section 71 stores the simulationprogram 72 and further stores a noise record 73 including measurementdata obtained under the respective known situations. Using a memory suchas a DRAM or an SRAM, the temporary storage section 74 temporarilystores data generated by processing carried out by the control section70.

The condition input section 75 is a user interface including a mouse, akeyboard, a display, etc., and a user is allowed to input, via thecondition input section 75, simulation conditions for an in-vehicle PLCsystem to be simulated. Examples of the simulation conditions include: apower line length; the number of ECUs connected to a power line;positions thereof (e.g., lengths of power lines from a reference point);the number and positions of actuators; a time width of an object to besimulated; and a timing at which the actuator is operated in the timewidth.

The pseudonoise generation section 76 generates a pseudonoise based on astate sequence in a time width of an object to be simulated, and noisecharacteristics and impulsive noise frequencies in the state sequence.Specifically, a sequence of voltage values in the time width isgenerated from a state sequence that is a sequence of binary valuesindicative of whether or not the state is an impulsive noise generatedstate, and from noise power of the impulsive noise when the impulsivenoise is generated.

The control section 70 obtains noise characteristics in the respectivesituations from the stored noise record 73. More specifically, based onthe simulation program 72, the control section 70 is capable ofperforming functions similar to those of the parameter estimationsection 601 of the control section 60 according to Embodiment 1 or 2,and is thus capable of obtaining, as the noise characteristics,respective parameters (i.e., a channel memory γ, an impulsive noiseoccurrence probability P₁, an impulse-to-background noise ratio R, and abackground noise power σ_(G) ²).

Further, the control section 70 obtains the impulsive noise frequenciesin the respective situations from the noise characteristics calculatedfor the measurement data under the respective situations, and from thestored noise record 73. More specifically, in addition to functionssimilar to those of the impulsive noise detection section 605 of thecontrol section 60 according to Embodiment 1 or 2, the control section70 is capable of performing, based on the simulation program 72, notonly impulsive noise detection but also a fast Fourier transformfunction, and is thus capable of obtaining the frequencies of thedetected impulsive noises. The control section 70 adds the calculatedimpulsive noise frequencies to the noise record 73.

Using the pseudonoise generation section 76, the control section 70generates a state sequence responsive to an actuator operation in a timewidth of a simulation object based on: the noise characteristicscalculated for measurement data under the respective situations; and thesimulation conditions inputted through the condition input section 75,and generates a pseudonoise based on the generated state sequence, andthe state noise power and impulsive noise frequency of each state.

FIG. 25 is an explanatory diagram illustrating exemplary details of thenoise record 73 stored in the storage section 71 of the simulationapparatus 7 according to Embodiment 3.

As illustrated in FIG. 25, the noise record 73 includes situationdetails and measurement data. The noise record 73 further includes:parameters (i.e., the channel memory γ, impulsive noise occurrenceprobability P₁, impulse-to-background noise ratio R, and backgroundnoise power σ_(G) ²) indicative of noise characteristics calculated byafter-mentioned processing; and frequencies of impulsive noises. Thenoise record 73 may include generated state sequences in advance. FIG.25 illustrates: measurement data in the period(measurement periodicalunit) of 0 to 1 msec from an operation of a door lock serving as one ofactuators; and exemplary details of the parameters indicative of thenoise characteristics. In this manner, noise data for each period underthe known situation is stored as the noise record 73, thus allowing asimulation to be executed later.

FIG. 26 is a flow chart illustrating an example of a procedure ofprocessing executed by the simulation apparatus 7 according toEmbodiment 3. It should be noted that, of the following processing stepsillustrated in the flow chart of FIG. 26, the steps common to thoseillustrated in the flow chart of FIG. 9 according to Embodiment 1 areidentified by the same step numbers, and detailed description thereofwill be omitted.

The control section 70 obtains measurement data (observed noisesequence) under each known situation, which is stored in the storagesection 71 (Step S21), and extracts data for a communication cycle fromthe measurement data (Step S2). It should be noted that when themeasurement data, already extracted by a communication cycle length, isstored as illustrated in the exemplary details of FIG. 25, Step S2 maybe skipped.

The control section 70 performs functions similar to those of theparameter estimation section 601 of the control section 60 according toEmbodiment 1 or 2, thereby calculating parameters θ (noisecharacteristics) for maximization of the likelihood of the data(observed noise sequence) extracted in Step S2 (Step S3). Thecalculation details are similar to those described with reference to theflow chart of FIG. 10 according to Embodiment 1 (or the flow charts ofFIGS. 17 to 20 according to Embodiment 2).

Further, the control section 70 performs functions similar to those ofthe parameter output section 604 of the control section 60 according toEmbodiment 1 or 2, thereby estimating and calculating an estimated statematrix (Step S4). Furthermore, the control section 70 performs functionssimilar to those of the impulsive noise detection section 605 of thecontrol section 60 according to Embodiment 1, thereby detecting animpulsive noise in the data extracted in Step S2 (Step S7). Then, usingthe fast Fourier transform function, the control section 70 calculatesthe frequency of the detected impulsive noise (Step S22).

The control section 70 stores the noise characteristics and impulsivenoise frequency calculated for the measurement data obtained under eachsituation so that the noise characteristics and impulsive noisefrequency are included as noise data in the noise record 73 (Step S23),and the control section 70 determines whether or not noisecharacteristics and impulsive noise frequencies are calculated for themeasurement data obtained under all the known situations (Step S24).When it is determined that noise characteristics are not calculated forthe measurement data obtained under all the known situations (S24: NO),the control section 70 returns the processing to Step S21, and continuesthe processing for calculating noise characteristics and impulsive noisefrequencies for the measurement data under the other situations.

When it is determined that noise characteristics and impulsive noisefrequencies are calculated for the measurement data obtained under allthe known situations (S24: YES), the control section 70 inputssimulation conditions through the condition input section 75 (Step S25).It should be noted that when the calculations are completed for themeasurement data obtained under all the situations in Step S24 (S24:YES), the control section 70 may allow the unillustrated display toprovide a screen for recommending input of simulation conditions, forexample. The control section 70 generates a pseudonoise from the noisecharacteristics and impulsive noise frequency associated with eachsituation and calculated in advance in accordance with the inputtedsimulation conditions including, for example, a circuit configurationfor a power line, i.e., a length of the power line and the numbers andtypes of communication apparatuses and actuators connected thereto (StepS26). Thus, the control section 70 ends the simulation.

More specifically, in Step S26, the control section 70 identifies thesituation corresponding to the simulation conditions, and reads, fromthe noise record stored in the storage section 71, the noisecharacteristics (i.e., the channel memory γ, impulsive noise occurrenceprobability P₁, impulse-to-background noise ratio R, and backgroundnoise power σ_(G) ²) and impulsive noise frequency, which are calculatedfrom the measurement data associated with the identified situation.Using the parameters indicative of the read noise characteristics, thecontrol section 70 generates a state sequence responsive to an actuatoroperation in a time width of an object to be simulated. The noise power(σ₁ ², σ₀ ²) and impulsive noise frequency, included in the noisecharacteristics, are reflected in the generated state sequence, therebygenerating a pseudonoise. It should be noted that the time width of theobject to be simulated is set at 5 msec, for example. Furthermore, thecontrol section 70 generates a pseudonoise for the entire period of 5msec from: the state sequence, state noise power and impulsive noisefrequency when one of the actuators is operated at a time point of 0msec in the time width; and the state sequence, state noise power andimpulsive noise frequency when the other actuator is operated at a timepoint of 2 msec in the time width.

As described above, for a physical configuration of in-vehicle PLCdifferent from one vehicle type to another, for example, the pseudonoiseof a noise generated in each power line can be generated with highaccuracy. Hence, modeling faithful to statistical properties ofimpulsive noises generated in the respective situations can be carriedout, thus making it possible to realize an efficient simulation at thestage of designing of an in-vehicle PLC system, and to implement thein-vehicle PLC that uses optimal frequency, communication method, etc.for effectively avoiding an impulsive noise.

Embodiment 4

In Embodiment 4, description will be made on an example of an apparatusfor calculating and recording noise characteristics and impulsive noisefrequencies under known situations as described in Embodiments 1 and 2,and for deciding an optimal communication method for a physicalconfiguration of an in-vehicle PLC system different depending on avehicle type, an option, etc. by using a pseudonoise generated by thesimulation apparatus 7 according to Embodiment 3, thereby enablingdesign of in-vehicle PLC.

FIG. 27 is a block diagram illustrating a configuration of an in-vehiclePLC design apparatus 8 according to Embodiment 4. The in-vehicle PLCdesign apparatus 8 includes: a control section 80; a storage section 81;a temporary storage section 85; an input/output section 86; apseudonoise generation section 87; and a communication simulationexecution section 88. Using a CPU, the control section 80 implementseach of after-mentioned functions based on an in-vehicle PLC designprogram 82 stored in the storage section 81. Using a nonvolatile memorysuch as a hard disk, an EEPROM or a flash memory, the storage section 81stores the in-vehicle PLC design program 82, and further stores a noiserecord 83 including noise characteristics and impulsive noise frequencycalculated from measurement data determined under each known situation.The storage section 81 further stores candidates for communicationconditions (communication condition candidate group 84) such ascommunication methods, communication frequencies or communicationparameters suitable for an in-vehicle PLC system to be designed inEmbodiment 4. Using a memory such as a DRAM or an SRAM, the temporarystorage section 85 temporarily stores data generated by processingcarried out by the control section 80.

The input/output section 86 is an interface that receives an operationalinput made by a designer, and outputs information to the designer. Theinput/output section 86 is connected with a keyboard 861, a mouse 862and a display 863. The input/output section 86 obtains informationinputted via the keyboard 861 or the mouse 862, notifies the controlsection 80 of the inputted information, and outputs characterinformation or image information to the display 863 based on aninstruction provided from the control section 80. Specifically, thecontrol section 80 is capable of receiving, via the input/output section86, a circuit configuration of the in-vehicle PLC system to be designed.In other words, via the input/output section 86, the control section 80receives information on a power line length, the numbers and types ofconnected communication apparatuses and actuators, etc. of thein-vehicle PLC system to be designed, which are inputted through anoperation performed on the keyboard 861 or the mouse 862 by thedesigner. Further, via the input/output section 86, the control section80 outputs, to the display 863, information on candidates forcommunication methods, communication frequencies or communicationparameters of each of them, included in the communication conditioncandidate group 84 stored in the storage section 81, and allows thecandidates to be selectively displayed on the display 863. From amongthe candidates displayed on the display 863, the designer selects anyone of the candidates by using the keyboard 861 or the mouse 862. Inthis case, the candidate selected by the keyboard 861 or the mouse 862can be identified by the control section 80.

The pseudonoise generation section 87 generates a pseudonoise based on astate sequence in a time width of an object to be simulated, and statenoise power and impulsive noise frequency of the state sequence.Specifically, a sequence of voltage values in the time width isgenerated based on: a state sequence that is a sequence of binary valuesindicative of whether or not the state is an impulsive noise generatedstate; noise power (σ₁ ², σ₀ ²) in each state indicative of whether ornot the state is an impulsive noise generated state; and an impulsivenoise frequency.

On the basis of the selected communication method, communicationfrequency and communication parameter included in the communicationcondition candidate group 84, the communication simulation executionsection 88 executes a communication simulation based on the givenpseudonoise, and outputs the simulation result. The result may be storedin the temporary storage section 85 or the storage section 81. For eachcandidate included in the communication condition candidate group 84,the control section 80 gives the pseudonoise, generated by thepseudonoise generation section 87, to the communication simulationexecution section 88 to execute a communication simulation.

From the results of the communication simulations executed for therespective candidates of the communication conditions, the controlsection 80 obtains communication error rates based on the in-vehicle PLCdesign program 82. Then, the control section 80 makes comparisons on thecommunication error rates calculated for the respective candidates, andidentifies, as the optimal candidate, the candidate having the lowesterror rate.

Processing executed by the control section 80 of the in-vehicle PLCdesign apparatus 8 configured as described above will be described withreference to a flow chart. FIG. 28 is a flow chart illustrating anexample of a procedure of the processing executed by the in-vehicle PLCdesign apparatus 8 according to Embodiment 4.

The control section 80 receives, via the input/output section 86,information on a system circuit configuration to be designed (Step S31),and reads, from the noise record 83 stored in the storage section 81,noise data (including noise characteristics and impulsive noisefrequency) calculated under each situation corresponding to the receivedcircuit configuration (Step S32). From the noise characteristics andimpulsive noise frequency of the noise data read in Step S32, thecontrol section 80 generates a pseudonoise by the pseudonoise generationsection 87 (Step S33).

Via the input/output section 86, the control section 80 receives inputof candidates for communication conditions including communicationmethods, communication frequencies or communication parameters of eachof them, which are included in the communication condition candidategroup 84 (Step S34). Specifically, the control section 80 allows thedisplay 863 to provide a screen for receiving input of the candidates,and receives an input made by the keyboard 861 or the mouse 862.

Using the generated pseudonoise, the control section 80 gives theinputted communication condition candidates to the communicationsimulation execution section 88, and allows the communication simulationexecution section 88 to execute a communication simulation (Step S35).

From results of the communication simulation executed in Step S35, thecontrol section 80 calculates a communication error rate (Step S36). Thecontrol section 80 calculates the communication error rate for each ofthe inputted candidates, and makes comparisons on the respectivecommunication error rates, thereby identifying an optimal communicationcondition candidate (Step S37). The control section 80 outputsinformation on the communication condition candidate, identified in StepS37, to the display 863 via the input/output section 86 so as to displaythe information on the display 863 (Step S38), thus ending theprocessing.

As a result of the above-described processing, based on an observednoise sequence obtained from observation under each situation, apseudonoise for reproducing an impulsive noise faithful to thestatistical properties of the impulsive noise is generated on the basisof the noise characteristics and frequency of the impulsive noiseestimated automatically using the statistical properties of the observednoise sequence itself. Since communication simulations are executed bythe in-vehicle PLC design apparatus 8 according to Embodiment 4 usingthe generated pseudonoise and the communication conditions (e.g.,communication methods, communication frequencies and communicationparameters) serving as candidates, detailed preliminary studies can beconducted on an effective communication method and the like thatminimize the influence of an impulsive noise different depending on anactual vehicle type or option, for example.

Embodiment 5

In Embodiment 5, description will be made on an example of an in-vehiclePLC system including an optimization apparatus for identifying optimalcommunication method, communication frequency and communicationparameter. The optimization apparatus detects an impulsive noise in thein-vehicle PLC system including the optimization apparatus itself,learns characteristics of the noise, and decides optimal communicationmethod, communication frequency and communication parameter. In thein-vehicle PLC system, settings are made, for example, at the end of atest and/or at the time of a vehicle inspection after vehicle assemblyso that communication is performed in accordance with communicationconditions identified by the optimization apparatus.

FIG. 29 is a block diagram illustrating a configuration of an in-vehiclePLC system according to Embodiment 5. It should be noted thatconstituent elements of the in-vehicle PLC system according toEmbodiment 5 other than an optimization apparatus 9 are common to thoseof the in-vehicle PLC system according to Embodiment 1. The commonconstituent elements are identified by the same reference characters asthose used in Embodiment 1, and detailed description thereof will beomitted.

The in-vehicle PLC system according to Embodiment 5 is configured toinclude: ECUs 1, 1, . . . ; actuators 2, 2, . . . operated in responseto control data transmitted from the ECUs 1, 1, . . . ; power lines 3,3, . . . through which electric power is supplied to each of the ECUs 1,1, . . . and the actuators 2, 2, . . . ; a battery 4 for supplyingelectric power to respective devices through the power lines 3, 3, . . .; a junction box 5 for branching and junction of the power lines 3, 3, .. . ; and the optimization apparatus 9 for optimizing communicationperformed in the in-vehicle PLC system. Also in Embodiment 5, the ECUs1, 1, . . . perform communication in accordance with a FlexRay protocolvia the power lines 3, 3, . . . .

As illustrated in FIG. 29, the optimization apparatus 9 according toEmbodiment 5 is connected to any given points of the power lines 3, 3, .. . . The optimization apparatus 9 obtains a feature of an impulsivenoise based on results of measurement of voltage values (signal levels)obtained at a predetermined interval in each power line 3, and performsthe function of deciding, from the calculated feature, optimalcommunication method, communication frequency and other parameters forthe respective power lines 3, 3, . . . .

FIG. 30 is a block diagram illustrating an internal configuration of theoptimization apparatus 9 included in the in-vehicle PLC system accordingto Embodiment 5. The optimization apparatus 9 includes: a controlsection 90; a storage section 91; a temporary storage-section 93; and ameasurement section 94. Using a CPU, the control section 90 executes anoptimization process based on an optimization program 92 stored in thestorage section 91. Using a nonvolatile memory such as a hard disk, anEEPROM or a flash memory, the storage section 91 stores the optimizationprogram 92, and further stores an impulsive noise feature 95 calculatedfor a detected noise. The storage section 91 further stores candidatesfor communication conditions (communication condition candidate group96) such as communication methods, communication frequencies orcommunication parameters suitable for the in-vehicle PLC systemaccording to Embodiment 5. Using a memory such as a DRAM or an SRAM, thetemporary storage section 93 temporarily stores data generated byprocessing carried out by the control section 90.

The measurement section 94 measures voltage values in the power lines 3,3, . . . at a predetermined interval, and stores the measurement resultsin the storage section 91 or the temporary storage section 93. Themeasurement section 94 may have a plurality of terminals so as to beable to measure voltage values at a plurality of measurement points inthe power lines 3. The predetermined interval(sampling interval) in themeasurement is 0.01 μsec (100 MHz), for example.

It should be noted that for the optimization apparatus 9, a personalcomputer may be used, or an FPGA, a DSP, an ASIC, etc., includingcomponents for performing functions of the respective constituentelements of the apparatus, may be used with the aim of providing theapparatus exclusively for noise detection and optimization.

Based on the optimization program 92, the control section 90 of theoptimization apparatus 9 performs the respective functions illustratedin FIG. 30, executes processing for detecting an impulsive noise fromvoltage values (observed noise sequence) measured and obtained at eachpredetermined interval by the measurement section 94, obtains a featureof the impulsive noise when the impulsive noise is detected, andexecutes processing for identifying optimal communication conditionsfrom this feature. FIG. 31 is a functional block diagram illustratingfunctions implemented by the optimization apparatus 9 included in thein-vehicle PLC system according to Embodiment 5.

Based on the optimization program 92, the control section 90 functionsas a parameter estimation section 901 for estimating a parameterassociated with a noise characteristic of the observed noise sequence,and also functions as an impulsive noise detection section 905 fordetermining and detecting the presence or absence of generation of animpulsive noise based on the estimated parameter. Functions of theparameter estimation section 901 include: a function of an initial valuedeciding section 902 for deciding an initial value of a parameter; afunction of the BW algorithm calculation section 903 for calculating,from the initial value, a noise characteristic for maximization of thelikelihood of the observed noise sequence by using a BW algorithm; and afunction of a parameter output section 904.

It should be noted that the functions of the parameter estimationsection 901 of the optimization apparatus 9 and the functions of theinitial value deciding section 902, BW algorithm calculation section 903and parameter output section 904 associated with the detailed functionsof the parameter estimation section 901 are identical to those of theparameter estimation section 601 of the control section 60 of the noisedetection apparatus 6 according to Embodiment 1 and those of the initialvalue deciding section 602, BW algorithm calculation section 603 andparameter output section 604 associated with the detailed functions ofthe parameter estimation section 601. Further, the functions of theimpulsive noise detection section 905 are also identical to those of theimpulsive noise detection section 605. Accordingly, detailed descriptionof these functions will be omitted.

Moreover, based on the optimization program 92, the control section 90also functions as: an impulsive noise feature calculation section 906for calculating a feature of a detected impulsive noise; and an optimalcandidate deciding section 907 for deciding an optimal communicationcondition based on the feature of the impulsive noise. It should benoted that the association between the impulsive noise feature and theoptimal candidate may be stored in advance in the storage section 91,and the control section 90 may make reference to the association by thefunction of the optimal candidate deciding section 907.

FIG. 32 is a flow chart illustrating an example of a procedure ofprocessing executed by the optimization apparatus 9 according toEmbodiment 5. It should be noted that, of the following processing stepsillustrated in the flow chart of FIG. 32, the processing steps fromSteps S1 to S8 are identical to those from Steps S1 to S8 illustrated inthe flow chart of FIG. 9 according to Embodiment 1. Alternatively, theprocessing steps of the flow chart of FIG. 32 from Steps S1 to S8 may beidentical to those of Steps S1, S2, S51 to S58, S7 and S8 illustrated inthe flow chart of FIG. 17 according to Embodiment 2. Accordingly, theprocessing steps of Steps S1 to S8 in FIG. 32 are illustrated in anabbreviated manner, and detailed description thereof will be omitted.

It should be noted that the following processing steps are performed onan as-needed basis during a test at the time of vehicle assembly orafter shipment.

The control section 90 obtains measurement data (observed noisesequence) by the measurement section 94 (Step S1), and extracts data inunits of communication cycles of FlexRay from the measurement data (StepS2). Also in Embodiment 5, the period(measurement periodical unit) ofthe extracted data is 1 msec. The extracted data is a sequence ofvoltage values for 100000 samples (K=100000).

When an impulsive noise is generated in the extracted data extracted for1 msec (K=100000 samples of voltage values obtained on the time series)(S6: YES, or S53: YES), the control section 90 calculates noisecharacteristics that are based on a hidden Markovian-Gaussian noisemodel (Step S3 or S57), calculates an estimated state sequence (Step S4or S58), detects an impulsive noise at each time point (Step 7), andthen stores noise data in the storage section 91 (Step 8). In this case,the noise data includes parameters (i.e., a channel memory γ, animpulsive noise occurrence probability P₁, an impulse-to-backgroundnoise ratio R, and a background noise power σ_(G) ²) indicative of thenoise characteristics calculated in Step S3. It should be noted that thenoise data may include the extracted data, or may include the estimatedstate sequence.

Next, from the parameters θ (noise characteristics) calculated in StepS3, the control section 90 calculates a noise feature (Step S41). Thenoise feature may be calculated using impulsive noise data included inthe data extracted in the period, or may be calculated using theestimated state sequence. Examples of the noise feature include animpulsive noise frequency, and an impulsive noise generation intervalcycle.

The control section 90 stores the noise feature, calculated in Step S41,in the storage section 91, and adds the noise feature to the impulsivenoise feature 95 (Step S42). Thus, the impulsive noise feature 95 in thestorage section 91 is updated. The control section 90 may delete an oldfeature of the past.

Based on the updated impulsive noise feature 95 in the storage section91, the control section 90 identifies an optimal candidate from thecommunication condition candidate group 96 (Step S43), and stores theidentified candidate in the storage section 91 (Step S44), thus endingthe processing.

As a result of the processing illustrated in the flow chart of FIG. 32and performed by the optimization apparatus 9, data of impulsive noises,actually generated in the in-vehicle PLC system of a vehicle afterassembly or after shipment, is accumulated. Then, based on theaccumulated data, the optimal communication method, communicationfrequency or other communication parameters for favorably performingcommunication by avoiding an impulsive noise generated in the vehicleare identified by the processing performed by the optimization apparatus9. Since the identified communication method, communication frequency orcommunication parameters is/are stored in the storage section 91,settings are made after assembly or at the time of a vehicle inspection,for example, thus making it possible to provide in-vehicle PLC that usesthe optimal frequency, communication method, etc. for effectivelyavoiding an impulsive noise afterwards. Accordingly, the communicationmethod, frequency and parameters, which minimize the influence of animpulsive noise, may be suitably selected in accordance with a situationchangeable with time.

Embodiment 6

In Embodiment 6, an example of an in-vehicle PLC system for performingcommunication while avoiding the frequency of a generated impulsivenoise will be described.

FIG. 33 is a block diagram illustrating a configuration of an in-vehiclePLC system according to Embodiment 6. It should be noted thatconstituent elements of the in-vehicle PLC system according toEmbodiment 6 other than an analysis apparatus 100, filter sections 20and details of respective ECUs are common to those of the in-vehicle PLCsystem according to Embodiment 1. The common constituent elements areidentified by the same reference characters as those used in Embodiment1, and detailed description thereof will be omitted.

The in-vehicle PLC system according to Embodiment 6 is configured toinclude: ECUs 1, 1, . . . ; actuators 2, 2, . . . operated in responseto control data transmitted from the ECUs 1, 1, . . . ; power lines 3,3, . . . through which electric power is supplied to each of the ECUs 1,1, . . . and the actuators 2, 2, . . . ; a battery 4 for supplyingelectric power to respective devices through the power lines 3, 3, . . .; a junction box 5 for branching and junction of the power lines 3, 3, .. . ; the analysis apparatus 100 for analyzing an impulsive noise in thein-vehicle PLC system; and a plurality of the filter sections 20, 20, .. . connected to the respective power lines 3, 3, . . . . Also inEmbodiment 6, the ECUs 1, 1, . . . perform communication in accordancewith a FlexRay protocol via the power lines 3, 3, . . . .

As illustrated in FIG. 33, the analysis apparatus 100 according toEmbodiment 6 is connected to any given points of the power lines 3, 3, .. . . The analysis apparatus 100 obtains an impulsive noise frequencybased on results of measurement of signal levels (voltage values)obtained at a predetermined interval in each power line 3. Based on thecalculated frequency, the analysis apparatus 100 adjusts the frequencyof a carrier wave for communication between the ECUs 1, 1, . . . , andadaptively controls a band rejection filter included in each filtersection 20.

FIG. 34 is a block diagram illustrating an internal configuration of thefilter section 20 included in the in-vehicle PLC system according toEmbodiment 6. The filter section 20 includes: a band rejection filter(BRF) 2E an AGC (Automatic Gain Control) amplifier 22; and an A/Dconverter 23.

Based on an instruction provided from a control section of the analysisapparatus 100, the band rejection filter 21 is capable of adjusting afrequency to be limited. The AGC amplifier 22 automatically adjusts again even when a frequency of a carrier wave is changed.

FIG. 35 is a block diagram illustrating an internal configuration of theanalysis apparatus 100 included in the in-vehicle PLC system accordingto Embodiment 6. The analysis apparatus 100 includes: a control section101; a storage section 102; a temporary storage section 104; ameasurement section 105; and an adjustment section 106. Using a CPU, thecontrol section 101 executes, based on an analysis program 103 stored inthe storage section 102, processing such as a process for detecting animpulsive noise generated in the power line 3 or a process forestimating the frequency of a generated impulsive noise to change thefrequency of a carrier wave. Using a nonvolatile memory such as a harddisk, an EEPROM or a flash memory, the storage section 102 stores theanalysis program 103. The storage section 102 further stores impulsivenoise frequency information 107. The impulsive noise frequencyinformation 107 includes: information for defining a plurality ofdifferent known situations; and impulsive noise frequencies associatedwith this information. Using a memory such as a DRAM or an SRAM, thetemporary storage section 104 temporarily stores data generated byprocessing carried out by the control section 101.

The measurement section 105 measures signal levels (voltage values) inthe power lines 3, 3, . . . at a predetermined interval, and stores themeasurement results in the storage section 102 or the temporary storagesection 104. The measurement section 105 may have a plurality ofterminals so as to be able to measure signal levels at a plurality ofmeasurement points in the power lines 3. The predeterminedinterval(sampling interval) in the measurement is 0.01 μsec (100 MHz),for example.

The adjustment section 106 is connected to each of the ECUs 1, 1, . . ., and to the band rejection filter 21 of each filter section 20. Inresponse to control from the control section 101, the adjustment section106 notifies each ECU 1 of the impulsive noise frequency so as to adjustfrequencies of local oscillators of a modulator and a demodulatorcontained in a transmitter-receiver of a power line communicationsection 13. Further, in response to control from the control section101, the adjustment section 106 adaptively controls the band rejectionfilters 21 with the aim of limiting the impulsive noise frequency.

For the analysis apparatus 100, a personal computer may be used, or anFPGA, a DSP, an ASIC, etc., including components for performingfunctions of the respective constituent elements of the apparatus, maybe used with the aim of providing the apparatus exclusively for noisedetection and frequency adjustment.

Based on the analysis program 103, the control section 101 of theanalysis apparatus 100 performs each function illustrated in FIG. 36,and executes a process for detecting an impulsive noise from the signallevels (observed noise sequence) measured and obtained at eachpredetermined interval by the measurement section 105. The controlsection 101 detects an impulsive noise in advance during a test at thetime of vehicle assembly, and obtains and stores the frequency of thedetected impulsive noise. Alternatively, the control section 101 maydetect an impulsive noise and obtain the frequency thereof on anas-needed basis during communication after vehicle shipment.

Furthermore, using the adjustment section 106, the control section 101adjusts a carrier wave frequency based on the calculated and storedimpulsive noise frequency, and executes a process for adaptivelycontrolling the band rejection filters 21. The control section 101performs the adjustment process in advance at the time of assembly.Alternatively, the control section 101 may detect an impulsive noisegenerated at any time, may obtain the frequency of the detectedimpulsive noise in real time, and then may perform the adjustmentprocess. Optionally, the control section 101 may read, from the storagesection 102, the impulsive noise frequency information 107 stored for animpulsive noise detected in advance, and may perform adjustment inaccordance with a situation.

FIG. 36 is a functional block diagram illustrating functions implementedby the analysis apparatus 100 included in the in-vehicle PLC systemaccording to Embodiment 6. Based on the analysis program 103, thecontrol section 101 functions as a parameter estimation section 1001 forestimating a parameter associated with a noise characteristic of theobserved noise sequence, and also functions as an impulsive noisedetection section 1005 for determining and detecting the presence orabsence of generation of an impulsive noise based on the estimatedparameter. Functions of the parameter estimation section 1001 include: afunction of an initial value deciding section 1002 for deciding aninitial value of a parameter; a function of a BW algorithm calculationsection 1003 for calculating, from the initial value, a noisecharacteristic for maximization of the likelihood of the observed noisesequence by using a BW algorithm; and a function of a parameter outputsection 1004.

The functions of the parameter estimation section 1001 of the analysisapparatus 100 and the functions of the initial value deciding section1002, BW algorithm calculation section 1003 and parameter output section1004 associated with the detailed functions of the parameter estimationsection 1001 are identical to those of the parameter estimation section601 of the control section 60 of the noise detection apparatus 6according to Embodiment 1 and those of the initial value decidingsection 602, BW algorithm calculation section 603 and parameter outputsection 604 associated with the detailed functions of the parameterestimation section 601. Further, the functions of the impulsive noisedetection section 1005 are also identical to those of the impulsivenoise detection section 605. Accordingly, detailed description of thesefunctions will be omitted.

Moreover, based on the analysis program 103, the control section 101also functions as a frequency calculation section 1006 for calculating afrequency of a detected impulsive noise. The impulsive noise frequency,calculated by the function of the frequency calculation section 1006, isstored as the impulsive noise frequency information 107 in the storagesection 102 by the control section 101.

FIG. 37 is a flow chart illustrating an example of a procedure ofprocessing executed by the analysis apparatus 100 according toEmbodiment 6. It should be noted that, of the following processing stepsillustrated in the flow chart of FIG. 37, the processing steps fromSteps S1 to S7 are identical to those from Steps S1 to S7 illustrated inthe flow chart of FIG. 9 according to Embodiment 1. Alternatively, theprocessing steps of the flow chart of FIG. 37 from Steps S1 to S7 may beidentical to those of Steps S1, S2, S51 to S58, and S7 illustrated inthe flow chart of FIG. 17 according to Embodiment 2. Accordingly, theprocessing steps of Steps 51 to S7 in FIG. 37 are illustrated in anabbreviated manner, and detailed description thereof will be omitted.

The control section 101 obtains measurement data (observed noisesequence) by the measurement section 105 (Step S1), and extracts data inunits of communication cycles of FlexRay from the measurement data (StepS2). Also in Embodiment 6, the period of the extracted data is 1 msec.The extracted data is a sequence of voltage values for 100000 samples(K=100000).

When an impulsive noise is generated in the extracted data extracted for1 msec (K=100000 samples of voltage values obtained on the time series)(S6: YES, or S53: YES), the control section 101 calculates noisecharacteristics that are based on a hidden Markovian-Gaussian noisemodel (Step S3 or S57), calculates an estimated state sequence (Step S4or S58), and detects an impulsive noise at each time point (Step S7).

From the parameters θ (noise characteristics) calculated in Step S3, thecontrol section 101 calculates a noise frequency (Step S91). When asituation is known, the calculated frequency is stored as the impulsivenoise frequency information 107 in the storage section 102 inassociation with a definition representing the situation (Step S92).

The control section 101 reads the stored frequency and uses theadjustment section 106 to notify a transmitter-receiver in each ECU 1 ofthis frequency so as to adjust frequencies of local oscillators of amodulator and a demodulator contained in the transmitter-receiver (StepS93). With the aim of limiting the impulsive noise frequency, thecontrol section 101 adaptively controls the band rejection filters 21 byusing the adjustment section 106 (Step S94), thus ending the processing.

Of the processing steps illustrated in the flow chart of FIG. 37, thecontrol section 101 may perform Steps S91 and S92 at the time of vehicleassembly in advance, and then may separately perform the processing ofSteps S93 and S94 in accordance with the situation of the in-vehicle PLCsystem.

As a result of the processing illustrated in the flow chart of FIG. 37and performed by the analysis apparatus 100, the frequencies ofimpulsive noises, actually generated in the in-vehicle PLC system of avehicle after assembly or after shipment, are accumulated as theimpulsive noise frequency information 107. Then, based on theaccumulated frequencies, the processing is performed by the controlsection 101 of the analysis apparatus 100, thereby enabling favorablecommunication by avoiding an impulsive noise generated in the vehicle.

In Embodiments 1 to 6, the forward and backward state probabilitiesprovided in FIG. 4 and the formulas 3 to 7 are used for a method forcalculating an a posteriori probability. As a method for calculatingparameters for maximization of the a posteriori probability, the othermethod may be used based on an EM method.

Furthermore, in Embodiments 1 to 6, detection of an impulsive noisegenerated in a power line (communication medium) has been describedusing an in-vehicle PLC system as an example. However, it is apparentthat the present invention is also applicable to detection of animpulsive noise generated in communication performed via a communicationline other than a power line. Moreover, the present invention is notonly applicable to detection of a noise generated in communication butalso applicable to detection of an impulsive noise generated in a signalline.

Note that the disclosed embodiments should be considered in all respectsas illustrative and not restrictive. The scope of the present inventionis defined by the appended claims rather than by the foregoingdescription, and all changes which come within the meaning and range ofequivalency of the claims are therefore intended to be embraced therein.

1. A noise detecting method for detecting a noise occurred in a communication medium, the method comprising the steps of: (a) measuring a signal level in the communication medium at a predetermined interval; (b) extracting, as an observed noise sequence, a measurement result for a predetermined measurement periodical unit; (c) calculating, from the extracted observed noise sequence, a noise characteristic by using a hidden Markovian-Gaussian noise model; (d) calculating, from the calculated noise characteristic and the observed noise sequence, an estimated state sequence that is a sequence indicative of whether or not a state is a noise-occurred state; and (e) individually detecting, from the estimated state sequence, an impulsive noise at each time point within the measurement periodical unit.
 2. The noise detecting method according to claim 1, wherein the step (d) comprises the steps of: (d1) calculating a posteriori probability of a state at each time point by using the extracted observed noise sequence, and the noise characteristic calculated for each measurement periodical unit; and (d2) estimating a state sequence that maximizes the calculated a posteriori probability of the state.
 3. The noise detecting method according to claim 1, the method further comprising the step (f) of determining, based on the calculated noise characteristic, the presence or absence of occurrence of an impulsive noise for the measurement periodical unit.
 4. The noise detecting method according to claim 1, wherein the measurement result for a communication periodical unit that is based on a communication method for the communication medium is extracted as the observed noise sequence.
 5. The noise detecting method according to claim 1, wherein the estimated state sequence is calculated as a sequence including a state, in which an impulsive noise is occurred, or a state, in which no impulsive noise is occurred, for each section equivalent to one or a plurality of the intervals.
 6. The noise detecting method according to claim 1, wherein the communication medium is an electronic power line arranged in a vehicle.
 7. The noise detecting method according to claim 2, wherein the step (d1) comprises the steps of: (d1-1) calculating a forward state probability related to a state preceding the state at each time point, and a backward state probability related to a state subsequent to the state at each time point; and (d1-2) calculating a posteriori probability of the state at each time point by using the calculated forward and backward state probabilities.
 8. The noise detecting method according to claim 2, wherein in the step (d), the estimated state sequence is calculated by using the observed noise sequence, and a temporal concentration of an impulsive noise, an impulsive noise occurrence probability, an impulsive-to-background noise ratio, and a background noise power in the measurement periodical unit so that the a posteriori probability of the state is maximized.
 9. The noise detecting method according to claim 3, wherein in the step (f), the determination is made based on at least one of a temporal concentration of an impulsive noise, an impulsive noise occurrence probability, an impulsive-to-background noise ratio, and a background noise power in the measurement periodical unit.
 10. The noise detecting method according to claim 7, wherein the step (c) comprises the steps of: (c1) deciding an initial value of state noise power of each of two states, i.e., a state in which an impulsive noise is occurred and a state in which no impulsive noise is occurred, based on the hidden Markovian-Gaussian noise model; (c2) deciding an initial value of each of four state transition probabilities between the two states; (c3) calculating forward and backward state probabilities by using the observed noise sequence, and the decided initial values of the state transition probabilities and state noise power; (c4) deciding, from the calculated forward and backward state probabilities, the four state transition probabilities in the measurement periodical unit, and the state noise power of each of the two states; (c5) repeating the step (c3) and the step (c4); (c6) calculating, in the course of the step (c5), the state transition probabilities and state noise power for maximization of the likelihood of the observed noise sequence; and (c7) identifying the noise characteristic by the calculated state transition probabilities and state noise power.
 11. The noise detecting method according to claim 10, wherein the step (c1) comprises the steps of; (c1-1) calculating three moments of a moment method from the observed noise sequence in the measurement periodical unit; and (c1-2) calculating, from the calculated three moments, the initial value of the state noise power of each of the two states.
 12. The noise detecting method according to claim 10, wherein the step (c2) comprises the steps of; (c2-1) calculating a threshold for a signal level value of the observed noise sequence from the calculated initial value of the state noise power of each of the two states; (c2-2) making a comparison between the calculated threshold and each signal level value of the observed noise sequence; (c2-3) calculating, as an estimated state sequence, a result of comparisons made on the respective signal level values, the result indicating which signal level value is higher or lower; and (c2-4) calculating the initial values of the four state transition probabilities from the calculated estimated state sequence.
 13. The noise detecting method according to claim 10, the method further comprising the steps of: (g) determining whether or not an impulsive noise occurrence probability in the measurement periodical unit falls within a predetermined range; (h) calculating, when the impulsive noise occurrence probability is determined to fall within the predetermined range, a predetermined statistical information criterion by using either one or both of the decided initial value of each of the four state transition probabilities and the initial value of the state noise power of each of the two states; (i) determining, based on the calculated statistical information criterion, whether or not an impulsive noise is included; and (j) skipping the step (c6) when it is determined that no impulsive noise is included.
 14. The noise detecting method according to claim 13, wherein the step (h) comprises the step (h1) of defining, as the statistical information criterion, one or a plurality of; a logarithmic likelihood; a Takeuchi information criterion; an Akaike information criterion; and a criterion that is based on the number of free parameters in the Takeuchi information criterion or Akaike information criterion.
 15. The noise detecting method according to claim 14, wherein the criterion that is based on the number of free parameters is whether or not the following expression is satisfied; $\begin{matrix} {{\frac{E_{K}\left\lfloor n_{k}^{4} \right\rfloor}{2\; K{\hat{\sigma}}^{4}} - \frac{1}{2}} > {1 + z}} & \left\lbrack {{Exp}.\mspace{14mu} 1} \right\rbrack \end{matrix}$ K: Information Length of Observed Noise Sequence E_(K): Sample Mean of Sequence Including K Samples n_(k): Signal Level Value at Time Point k in Observed Noise Sequence {circumflex over (σ)}²: Weighted Distribution of Two States (={circumflex over (P)}₀{circumflex over (σ)}₀ ²+{circumflex over (P)}₁{circumflex over (σ)}₁ ²) (where {circumflex over (σ)}₀, {circumflex over (σ)}₁: Estimated Values of Noise Standard Deviations of Two States {circumflex over (P)}₀, {circumflex over (P)}₁: Estimated Values of Probabilities of Two States), and z: Any value meets z>0
 16. A noise detecting apparatus for detecting a noise occurred in a communication medium, the apparatus comprising: a measurement section for measuring a signal level in the communication medium at a predetermined interval; an extraction section for extracting, as an observed noise sequence, a measurement result for a predetermined measurement periodical unit; a calculation section for calculating, from the extracted observed noise sequence, a noise characteristic by using a hidden Markovian-Gaussian noise model; an estimation section for calculating, from the calculated noise characteristic and the observed noise sequence, an estimated state sequence that is a sequence indicative of whether or not a state is a noise-occurred state; and a detection section for individually detecting, from the estimated state sequence, each impulsive noise at each time point within the measurement periodical unit.
 17. The noise detection apparatus according to claim 16, wherein the estimation section calculates, using the extracted observed noise sequence and the noise characteristic calculated for the measurement periodical unit, a forward state probability related to a state preceding a state at each time point, and a backward state probability related to a state subsequent to the state at each time point, wherein the estimation section calculates an a posteriori probability of the state at each time point by using the calculated forward and backward state probabilities, wherein the estimation section repeats the calculation of the forward and backward state probabilities so as to maximize the calculated a posteriori probability, and wherein the estimation section calculates, as the estimated state sequence, a sequence of states at each time point at which the a posteriori probability is maximized.
 18. The noise detection apparatus according to claim 16, wherein the calculation section decides an initial value of state noise power of each of two states, i.e., a state in which an impulsive noise is occurred and a state in which no impulsive noise is occurred, based on the hidden Markovian-Gaussian noise model, wherein the calculation section decides an initial value of each of four state transition probabilities between the two states, wherein the calculation section calculates forward and backward state probabilities by using the observed noise sequence, and the decided initial values of the state transition probabilities and state noise power, wherein the calculation section decides, from the calculated forward and backward state probabilities, the four state transition probabilities in the measurement periodical unit, and the state noise power of each of the two states, wherein the calculation section repeats: the calculation of the forward and backward state probabilities with the use of the decided state transition probabilities and state noise power; and the decision of the state transition probabilities and state noise power from the calculated forward and backward state probabilities, wherein the calculation section calculates, with the repetition, the state transition probabilities and state noise power for maximization of the likelihood of the observed noise sequence, and wherein the calculation section identifies the noise characteristic by the calculated state transition probabilities and state noise power.
 19. The noise detection apparatus according to claim 16, wherein the communication medium is an electronic power line arranged in a vehicle.
 20. A simulation method for performing a simulation of a noise occurred in a communication medium, the method comprising the steps of: measuring a signal level in the communication medium in a plurality of different known situations at a predetermined interval; extracting, as an observed noise sequence, a measurement result for a predetermined measurement periodical unit; calculating, from the extracted observed noise sequence, a noise characteristic by using a hidden Markovian-Gaussian noise model; calculating, from the calculated noise characteristic and the observed noise sequence, an estimated state sequence that is a sequence indicative of whether or not a state is a noise-occurred state; individually detecting, from the estimated state sequence, each impulsive noise within the measurement unit period; calculating a frequency of the detected impulsive noise; recording the noise characteristic calculated for each the measurement periodical unit and the calculated impulsive noise frequency in association with the corresponding known situation; generating a state sequence that is based on a hidden Markov model, the state sequence being generated on the basis of a characteristic of the communication medium to be subjected to simulation and by using the noise characteristic associated with the situation corresponding to the characteristic of the communication medium; and generating a pseudonoise from the generated state sequence and the impulsive noise frequency.
 21. A simulation apparatus for performing a simulation of a noise occurred in a communication medium, the apparatus comprising: a measurement section for measuring a signal level in the communication medium in a plurality of different known situations at a predetermined interval; an extraction section for extracting, as an observed noise sequence, a measurement result for a predetermined measurement periodical unit; a calculation section for calculating, from the extracted observed noise sequence, a noise characteristic by using a hidden Markovian-Gaussian noise model; an estimation section for calculating, from the calculated noise characteristic and the observed noise sequence, an estimated state sequence that is a sequence indicative of whether or not a state is a noise-occurred state; a detection section for individually detecting, from the estimated state sequence, an impulsive noise at each time point within the measurement unit period; a frequency calculation section for calculating a frequency of the detected impulsive noise; a recording section for recording the noise characteristic calculated for each given measurement unit period and the calculated impulsive noise frequency in association with the corresponding known situation; a generation section for generating a state sequence that is based on a hidden Markov model, the state sequence being generated on the basis of a configuration of a communication system to be subjected to simulation and by using the noise characteristic associated with the situation corresponding to the configuration; and a pseudonoise generation section for generating a pseudonoise from the generated state sequence and the impulsive noise frequency.
 22. A communication system comprising: a plurality of communication apparatuses; a communication medium; an analysis apparatus; and a band rejection filter, wherein each communication apparatus comprises a data communication section for receiving and transmitting data via the communication medium, wherein the analysis apparatus comprises: a storage section in which a frequency of an impulsive noise and a plurality of different known situations are associated with each other; a reading section for reading a noise characteristic and the impulsive noise frequency from the storage section; a frequency adjustment section for adjusting a carrier wave frequency so that the carrier wave frequency falls within a frequency band different from that of the impulsive noise; and an filter adjustment section for adjusting the band rejection filter so as to suppress the impulsive noise, and wherein the impulsive noise frequency stored in the storage section is calculated by: measuring a signal level in the communication medium in each known situation at a predetermined interval on the time series; extracting, as an observed noise sequence, a measurement result for a predetermined measurement periodical unit; calculating, based on the extracted observed noise sequence, a noise characteristic for each measurement periodical unit by using a hidden Markovian-Gaussian noise model, and calculating an estimated state sequence from the noise characteristic and the observed noise sequence; and calculating the impulsive noise frequency from an impulsive noise detected from the estimated state sequence.
 23. The communication system according to claim 22, wherein the communication medium is an electronic power line arranged in a vehicle.
 24. A communication system comprising: a plurality of communication apparatuses; a communication medium; a band rejection filter; and a control device for the band rejection filter, wherein each communication apparatus comprises a data communication section for receiving and transmitting data via the communication medium, and wherein the control device comprises: a measurement section for measuring a signal level in the communication medium at a predetermined interval; an extraction section for extracting, as an observed noise sequence, a measurement result for a predetermined measurement periodical unit; a calculation section for calculating, from the extracted observed noise sequence, a noise characteristic for each measurement periodical unit based on a hidden Markovian-Gaussian noise model; a detection section for individually detecting each impulsive noise from an estimated state sequence estimated from the calculated noise characteristic and the observed noise sequence; and a adjustment section for adjusting and controlling, based on a frequency of the detected impulsive noise, a carrier wave frequency of each communication apparatus and the band rejection filter.
 25. The communication system according to claim 24, wherein the communication medium is an electronic power line arranged in a vehicle. 